Merge branch 'feature_bugfix/251104_event' of https://git.valuefrontier.cn/vf/vf_react into feature_bugfix/251104_event

* 'feature_bugfix/251104_event' of https://git.valuefrontier.cn/vf/vf_react: agent功能开发增加MCP后端 agent功能开发增加MCP后端 agent功能开发增加MCP后端 agent功能开发增加MCP后端 agent功能开发增加MCP后端 agent功能开发增加MCP后端 agent功能开发增加MCP后端
2025-11-07 19:48:20 +08:00
parent 00aabfacea 463ca7cf60
commit 0f3219143f
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--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -2956,8 +2956,59 @@ refactor(components): 将 EventCard 拆分为原子组件

 > **📝 页面级变更历史**: 特定页面的详细变更历史和技术文档已迁移到各自的文档中：
 > - **Community 页面**: [docs/Community.md](./docs/Community.md) - 页面架构、组件结构、数据流、变更历史
+> - **Agent 系统**: [AGENT_INTEGRATION_COMPLETE.md](./AGENT_INTEGRATION_COMPLETE.md) - Agent 系统集成完整说明
 > - **其他页面**: 根据需要创建独立的页面文档

+### 2025-11-07: Agent 系统集成到 mcp_server.py
+
+**影响范围**: 后端 MCP 服务器 + 前端 Agent 聊天功能
+
+**集成成果**:
+- 将独立的 Agent 系统完全集成到 `mcp_server.py` 中
+- 使用 **Kimi (kimi-k2-thinking)** 进行计划制定和推理
+- 使用 **DeepMoney (本地部署)** 进行新闻总结
+- 实现三阶段智能分析流程（计划→执行→总结）
+- 前端使用 ChatInterfaceV2 + 可折叠卡片展示执行过程
+- **无需运行多个脚本**，所有功能集成在单一服务中
+
+**技术要点**:
+- 新增 `MCPAgentIntegrated` 类（991-1367行）
+- 新增 `/agent/chat` API 端点
+- 新增特殊工具 `summarize_news`（使用 DeepMoney）
+- Kimi 使用 `reasoning_content` 字段记录思考过程
+- 自动替换占位符（"前面的新闻数据" → 实际数据）
+
+**前端组件**:
+- `ChatInterfaceV2.js` - 新版聊天界面
+- `PlanCard.js` - 执行计划展示
+- `StepResultCard.js` - 步骤结果卡片（可折叠）
+- 路由：`/agent-chat`
+
+**详细文档**: 参见 [AGENT_INTEGRATION_COMPLETE.md](./AGENT_INTEGRATION_COMPLETE.md)
+
+### 2025-10-30: EventList.js 组件化重构
+
+**影响范围**: Community 页面核心组件
+
+**重构成果**:
+- 将 1095 行的 `EventList.js` 拆分为 497 行主组件 + 10 个子组件
+- 代码行数减少 **54.6%** (598 行)
+- 创建了 7 个原子组件 (Atoms) 和 2 个组合组件 (Molecules)
+
+**新增组件**:
+- `EventCard/` - 统一入口，智能路由紧凑/详细模式
+  - `CompactEventCard.js` - 紧凑模式事件卡片
+  - `DetailedEventCard.js` - 详细模式事件卡片
+  - 7 个原子组件: EventTimeline, EventImportanceBadge, EventStats, EventFollowButton, EventPriceDisplay, EventDescription, EventHeader
+
+**新增工具函数**:
+- `src/utils/priceFormatters.js` - 价格格式化工具 (getPriceChangeColor, formatPriceChange, PriceArrow)
+- `src/constants/animations.js` - 动画常量 (pulseAnimation, fadeIn, slideInUp)
+
+**优势**: 提高了代码可维护性、可复用性、可测试性和性能
+
+**详细文档**: 参见 [docs/Community.md](./docs/Community.md)
+
 ---

 ## 更新本文档
--- a/pycache/mcp_server.cpython-310.pyc
+++ b/pycache/mcp_server.cpython-310.pyc
--- a/docs/AGENT_DEPLOYMENT.md
+++ b/docs/AGENT_DEPLOYMENT.md
@@ -0,0 +1,381 @@
+# AI Agent 系统部署指南
+
+## 🎯 系统架构
+
+### 三阶段流程
+
+```
+用户输入
+    ↓
+[阶段1: 计划制定 Planning]
+    - LLM 分析用户需求
+    - 确定需要哪些工具
+    - 制定执行计划（steps）
+    ↓
+[阶段2: 工具执行 Execution]
+    - 按计划顺序调用 MCP 工具
+    - 收集数据
+    - 异常处理和重试
+    ↓
+[阶段3: 结果总结 Summarization]
+    - LLM 综合分析所有数据
+    - 生成自然语言报告
+    ↓
+输出给用户
+```
+
+## 📦 文件清单
+
+### 后端文件
+
+```
+mcp_server.py              # MCP 工具服务器（已有）
+mcp_agent_system.py        # Agent 系统核心逻辑（新增）
+mcp_config.py              # 配置文件（已有）
+mcp_database.py            # 数据库操作（已有）
+```
+
+### 前端文件
+
+```
+src/components/ChatBot/
+├── ChatInterfaceV2.js     # 新版聊天界面（漂亮）
+├── PlanCard.js            # 执行计划卡片
+├── StepResultCard.js      # 步骤结果卡片（可折叠）
+├── ChatInterface.js       # 旧版聊天界面（保留）
+├── MessageBubble.js       # 消息气泡组件（保留）
+└── index.js               # 统一导出
+
+src/views/AgentChat/
+└── index.js               # Agent 聊天页面
+```
+
+## 🚀 部署步骤
+
+### 1. 安装依赖
+
+```bash
+# 进入项目目录
+cd /home/ubuntu/vf_react
+
+# 安装 OpenAI SDK（支持多个LLM提供商）
+pip install openai
+```
+
+### 2. 获取 LLM API Key
+
+**推荐：通义千问（便宜且中文能力强）**
+
+1. 访问 https://dashscope.console.aliyun.com/
+2. 注册/登录阿里云账号
+3. 开通 DashScope 服务
+4. 创建 API Key
+5. 复制 API Key（格式：`sk-xxx...`）
+
+**其他选择**：
+- DeepSeek: https://platform.deepseek.com/ （最便宜）
+- OpenAI: https://platform.openai.com/ （需要翻墙）
+
+### 3. 配置环境变量
+
+```bash
+# 编辑环境变量
+sudo nano /etc/environment
+
+# 添加以下内容（选择一个）
+# 方式1: 通义千问（推荐）
+DASHSCOPE_API_KEY="sk-your-key-here"
+
+# 方式2: DeepSeek（更便宜）
+DEEPSEEK_API_KEY="sk-your-key-here"
+
+# 方式3: OpenAI
+OPENAI_API_KEY="sk-your-key-here"
+
+# 保存并退出，然后重新加载
+source /etc/environment
+
+# 验证环境变量
+echo $DASHSCOPE_API_KEY
+```
+
+### 4. 修改 mcp_server.py
+
+在文件末尾（`if __name__ == "__main__":` 之前）添加：
+
+```python
+# ==================== Agent 端点 ====================
+
+from mcp_agent_system import MCPAgent, ChatRequest, AgentResponse
+
+# 创建 Agent 实例
+agent = MCPAgent(provider="qwen")  # 或 "deepseek", "openai"
+
+@app.post("/agent/chat", response_model=AgentResponse)
+async def agent_chat(request: ChatRequest):
+    """智能代理对话端点"""
+    logger.info(f"Agent chat: {request.message}")
+
+    # 获取工具列表和处理器
+    tools = [tool.dict() for tool in TOOLS]
+
+    # 处理查询
+    response = await agent.process_query(
+        user_query=request.message,
+        tools=tools,
+        tool_handlers=TOOL_HANDLERS,
+    )
+
+    return response
+```
+
+### 5. 重启 MCP 服务
+
+```bash
+# 如果使用 systemd
+sudo systemctl restart mcp-server
+
+# 或者手动重启
+pkill -f mcp_server
+nohup uvicorn mcp_server:app --host 0.0.0.0 --port 8900 > mcp_server.log 2>&1 &
+
+# 查看日志
+tail -f mcp_server.log
+```
+
+### 6. 测试 Agent API
+
+```bash
+# 测试 Agent 端点
+curl -X POST http://localhost:8900/agent/chat \
+  -H "Content-Type: application/json" \
+  -d '{
+    "message": "全面分析贵州茅台这只股票",
+    "conversation_history": []
+  }'
+
+# 应该返回类似这样的JSON：
+# {
+#   "success": true,
+#   "message": "根据分析，贵州茅台...",
+#   "plan": {
+#     "goal": "全面分析贵州茅台",
+#     "steps": [...]
+#   },
+#   "step_results": [...],
+#   "metadata": {...}
+# }
+```
+
+### 7. 部署前端
+
+```bash
+# 在本地构建
+npm run build
+
+# 上传到服务器
+scp -r build/* ubuntu@your-server:/var/www/valuefrontier.cn/
+
+# 或者在服务器上构建
+cd /home/ubuntu/vf_react
+npm run build
+sudo cp -r build/* /var/www/valuefrontier.cn/
+```
+
+### 8. 重启 Nginx
+
+```bash
+sudo systemctl reload nginx
+```
+
+## ✅ 验证部署
+
+### 1. 测试后端 API
+
+```bash
+# 测试工具列表
+curl https://valuefrontier.cn/mcp/tools
+
+# 测试 Agent
+curl -X POST https://valuefrontier.cn/mcp/agent/chat \
+  -H "Content-Type: application/json" \
+  -d '{
+    "message": "今日涨停股票有哪些",
+    "conversation_history": []
+  }'
+```
+
+### 2. 测试前端
+
+1. 访问 https://valuefrontier.cn/agent-chat
+2. 输入问题："全面分析贵州茅台这只股票"
+3. 观察：
+   - ✓ 是否显示执行计划卡片
+   - ✓ 是否显示步骤执行过程
+   - ✓ 是否显示最终总结
+   - ✓ 步骤结果卡片是否可折叠
+
+### 3. 测试用例
+
+```
+测试1: 简单查询
+输入：查询贵州茅台的股票信息
+预期：调用 get_stock_basic_info，返回基本信息
+
+测试2: 深度分析（推荐）
+输入：全面分析贵州茅台这只股票
+预期：
+  - 步骤1: get_stock_basic_info
+  - 步骤2: get_stock_financial_index
+  - 步骤3: get_stock_trade_data
+  - 步骤4: search_china_news
+  - 步骤5: summarize_with_llm
+
+测试3: 市场热点
+输入：今日涨停股票有哪些亮点
+预期：
+  - 步骤1: search_limit_up_stocks
+  - 步骤2: get_concept_statistics
+  - 步骤3: summarize_with_llm
+
+测试4: 概念分析
+输入：新能源概念板块的投资机会
+预期：
+  - 步骤1: search_concepts（新能源）
+  - 步骤2: search_china_news（新能源）
+  - 步骤3: summarize_with_llm
+```
+
+## 🐛 故障排查
+
+### 问题1: Agent 返回 "Provider not configured"
+
+**原因**: 环境变量未设置
+
+**解决**:
+```bash
+# 检查环境变量
+echo $DASHSCOPE_API_KEY
+
+# 如果为空，重新设置
+export DASHSCOPE_API_KEY="sk-xxx..."
+
+# 重启服务
+sudo systemctl restart mcp-server
+```
+
+### 问题2: Agent 返回 JSON 解析错误
+
+**原因**: LLM 没有返回正确的 JSON 格式
+
+**解决**: 在 `mcp_agent_system.py` 中已经处理了代码块标记清理，如果还有问题：
+1. 检查 LLM 的 temperature 参数（建议 0.3）
+2. 检查 prompt 是否清晰
+3. 尝试不同的 LLM 提供商
+
+### 问题3: 前端显示 "查询失败"
+
+**原因**: 后端 API 未正确配置或 Nginx 代理问题
+
+**解决**:
+```bash
+# 1. 检查 MCP 服务是否运行
+ps aux | grep mcp_server
+
+# 2. 检查 Nginx 配置
+sudo nginx -t
+
+# 3. 查看错误日志
+sudo tail -f /var/log/nginx/error.log
+tail -f /home/ubuntu/vf_react/mcp_server.log
+```
+
+### 问题4: 执行步骤失败
+
+**原因**: 某个 MCP 工具调用失败
+
+**解决**: 查看步骤结果卡片中的错误信息，通常是：
+- API 超时：增加 timeout
+- 参数错误：检查工具定义
+- 数据库连接失败：检查数据库连接
+
+## 💰 成本估算
+
+### 使用通义千问（qwen-plus）
+
+**价格**: ¥0.004/1000 tokens
+
+**典型对话消耗**:
+- 简单查询（1步）: ~500 tokens = ¥0.002
+- 深度分析（5步）: ~3000 tokens = ¥0.012
+- 平均每次对话: ¥0.005
+
+**月度成本**（1000次深度分析）:
+- 1000次 × ¥0.012 = ¥12
+
+**结论**: 非常便宜！1000次深度分析只需要12元。
+
+### 使用 DeepSeek（更便宜）
+
+**价格**: ¥0.001/1000 tokens（比通义千问便宜4倍）
+
+**月度成本**（1000次深度分析）:
+- 1000次 × ¥0.003 = ¥3
+
+## 📊 监控和优化
+
+### 1. 添加日志监控
+
+```bash
+# 实时查看 Agent 日志
+tail -f mcp_server.log | grep -E "\[Agent\]|\[Planning\]|\[Execution\]|\[Summary\]"
+```
+
+### 2. 性能优化建议
+
+1. **缓存计划**: 相似的问题可以复用执行计划
+2. **并行执行**: 独立的工具调用可以并行执行
+3. **流式输出**: 使用 Server-Sent Events 实时返回进度
+4. **结果缓存**: 相同的工具调用结果可以缓存
+
+### 3. 添加统计分析
+
+在 `mcp_server.py` 中添加：
+
+```python
+from datetime import datetime
+import json
+
+# 记录每次 Agent 调用
+@app.post("/agent/chat")
+async def agent_chat(request: ChatRequest):
+    start_time = datetime.now()
+
+    response = await agent.process_query(...)
+
+    duration = (datetime.now() - start_time).total_seconds()
+
+    # 记录到日志
+    logger.info(f"Agent query completed in {duration:.2f}s", extra={
+        "query": request.message,
+        "steps": len(response.plan.steps) if response.plan else 0,
+        "success": response.success,
+        "duration": duration,
+    })
+
+    return response
+```
+
+## 🎉 完成！
+
+现在你的 AI Agent 系统已经部署完成！
+
+访问 https://valuefrontier.cn/agent-chat 开始使用。
+
+**特点**:
+- ✅ 三阶段智能分析（计划-执行-总结）
+- ✅ 漂亮的UI界面（卡片式展示）
+- ✅ 步骤结果可折叠查看
+- ✅ 实时进度反馈
+- ✅ 异常处理和重试
+- ✅ 成本低廉（¥3-12/月）
--- a/docs/MCP_ARCHITECTURE.md
+++ b/docs/MCP_ARCHITECTURE.md
@@ -0,0 +1,309 @@
+# MCP 架构说明
+
+## 🎯 MCP 是什么？
+
+**MCP (Model Context Protocol)** 是一个**工具调用协议**，它的核心职责是：
+
+1. ✅ **定义工具接口**：告诉 LLM 有哪些工具可用，每个工具需要什么参数
+2. ✅ **执行工具调用**：根据请求调用对应的后端 API
+3. ✅ **返回结构化数据**：将 API 结果返回给调用方
+
+**MCP 不负责**：
+- ❌ 自然语言理解（NLU）
+- ❌ 意图识别
+- ❌ 结果总结
+- ❌ 对话管理
+
+## 📊 当前架构
+
+### 方案 1：简单关键词匹配（已实现）
+
+```
+用户输入："查询贵州茅台的股票信息"
+    ↓
+前端 ChatInterface (关键词匹配)
+    ↓
+MCP 工具层 (search_china_news)
+    ↓
+返回 JSON 数据
+    ↓
+前端显示原始数据
+```
+
+**问题**：
+- ✗ 只能识别简单关键词
+- ✗ 无法理解复杂意图
+- ✗ 返回的是原始 JSON，用户体验差
+
+### 方案 2：集成 LLM（推荐）
+
+```
+用户输入："查询贵州茅台的股票信息"
+    ↓
+LLM (Claude/GPT-4/通义千问)
+    ↓ 理解意图：需要查询股票代码 600519 的基本信息
+    ↓ 选择工具：get_stock_basic_info
+    ↓ 提取参数：{"seccode": "600519"}
+MCP 工具层
+    ↓ 调用 API，获取数据
+返回结构化数据
+    ↓
+LLM 总结结果
+    ↓ "贵州茅台（600519）是中国知名的白酒生产企业，
+       当前股价 1650.00 元，市值 2.07 万亿..."
+前端显示自然语言回复
+```
+
+**优势**：
+- ✓ 理解复杂意图
+- ✓ 自动选择合适的工具
+- ✓ 自然语言总结，用户体验好
+- ✓ 支持多轮对话
+
+## 🔧 实现方案
+
+### 选项 A：前端集成 LLM（快速实现）
+
+**适用场景**：快速原型、小规模应用
+
+**优点**：
+- 实现简单
+- 无需修改后端
+
+**缺点**：
+- API Key 暴露在前端（安全风险）
+- 每个用户都消耗 API 额度
+- 无法统一管理和监控
+
+**实现步骤**：
+
+1. 修改 `src/components/ChatBot/ChatInterface.js`：
+
+```javascript
+import { llmService } from '../../services/llmService';
+
+const handleSendMessage = async () => {
+  // ...
+
+  // 使用 LLM 服务替代简单的 mcpService.chat
+  const response = await llmService.chat(inputValue, messages);
+
+  // ...
+};
+```
+
+2. 配置 API Key（在 `.env.local`）：
+
+```bash
+REACT_APP_OPENAI_API_KEY=sk-xxx...
+# 或者使用通义千问（更便宜）
+REACT_APP_DASHSCOPE_API_KEY=sk-xxx...
+```
+
+### 选项 B：后端集成 LLM（生产推荐）⭐
+
+**适用场景**：生产环境、需要安全和性能
+
+**优点**：
+- ✓ API Key 安全（不暴露给前端）
+- ✓ 统一管理和监控
+- ✓ 可以做缓存优化
+- ✓ 可以做速率限制
+
+**缺点**：
+- 需要修改后端
+- 增加服务器成本
+
+**实现步骤**：
+
+#### 1. 安装依赖
+
+```bash
+pip install openai
+```
+
+#### 2. 修改 `mcp_server.py`，添加聊天端点
+
+在文件末尾添加：
+
+```python
+from mcp_chat_endpoint import MCPChatAssistant, ChatRequest, ChatResponse
+
+# 创建聊天助手实例
+chat_assistant = MCPChatAssistant(provider="qwen")  # 推荐使用通义千问
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    """智能对话端点 - 使用LLM理解意图并调用工具"""
+    logger.info(f"Chat request: {request.message}")
+
+    # 获取可用工具列表
+    tools = [tool.dict() for tool in TOOLS]
+
+    # 调用聊天助手
+    response = await chat_assistant.chat(
+        user_message=request.message,
+        conversation_history=request.conversation_history,
+        tools=tools,
+    )
+
+    return response
+```
+
+#### 3. 配置环境变量
+
+在服务器上设置：
+
+```bash
+# 方式1：使用通义千问（推荐，价格便宜）
+export DASHSCOPE_API_KEY="sk-xxx..."
+
+# 方式2：使用 OpenAI
+export OPENAI_API_KEY="sk-xxx..."
+
+# 方式3：使用 DeepSeek（最便宜）
+export DEEPSEEK_API_KEY="sk-xxx..."
+```
+
+#### 4. 修改前端 `mcpService.js`
+
+```javascript
+/**
+ * 智能对话 - 使用后端LLM处理
+ */
+async chat(userMessage, conversationHistory = []) {
+  try {
+    const response = await this.client.post('/chat', {
+      message: userMessage,
+      conversation_history: conversationHistory,
+    });
+
+    return {
+      success: true,
+      data: response,
+    };
+  } catch (error) {
+    return {
+      success: false,
+      error: error.message || '对话处理失败',
+    };
+  }
+}
+```
+
+#### 5. 修改前端 `ChatInterface.js`
+
+```javascript
+const handleSendMessage = async () => {
+  // ...
+
+  try {
+    // 调用后端聊天API
+    const response = await mcpService.chat(inputValue, messages);
+
+    if (response.success) {
+      const botMessage = {
+        id: Date.now() + 1,
+        content: response.data.message,  // LLM总结的自然语言
+        isUser: false,
+        type: 'text',
+        timestamp: new Date().toISOString(),
+        toolUsed: response.data.tool_used,  // 可选：显示使用了哪个工具
+        rawData: response.data.raw_data,    // 可选：原始数据（折叠显示）
+      };
+      setMessages((prev) => [...prev, botMessage]);
+    }
+  } catch (error) {
+    // ...
+  }
+};
+```
+
+## 💰 LLM 选择和成本
+
+### 推荐：通义千问（阿里云）
+
+**优点**：
+- 价格便宜（1000次对话约 ¥1-2）
+- 中文理解能力强
+- 国内访问稳定
+
+**价格**：
+- qwen-plus: ¥0.004/1000 tokens（约 ¥0.001/次对话）
+- qwen-turbo: ¥0.002/1000 tokens（更便宜）
+
+**获取 API Key**：
+1. 访问 https://dashscope.console.aliyun.com/
+2. 创建 API Key
+3. 设置环境变量 `DASHSCOPE_API_KEY`
+
+### 其他选择
+
+| 提供商 | 模型 | 价格 | 优点 | 缺点 |
+|--------|------|------|------|------|
+| **通义千问** | qwen-plus | ¥0.001/次 | 便宜、中文好 | - |
+| **DeepSeek** | deepseek-chat | ¥0.0005/次 | 最便宜 | 新公司 |
+| **OpenAI** | gpt-4o-mini | $0.15/1M tokens | 能力强 | 贵、需翻墙 |
+| **Claude** | claude-3-haiku | $0.25/1M tokens | 理解力强 | 贵、需翻墙 |
+
+## 🚀 部署步骤
+
+### 1. 后端部署
+
+```bash
+# 安装依赖
+pip install openai
+
+# 设置 API Key
+export DASHSCOPE_API_KEY="sk-xxx..."
+
+# 重启服务
+sudo systemctl restart mcp-server
+
+# 测试聊天端点
+curl -X POST https://valuefrontier.cn/mcp/chat \
+  -H "Content-Type: application/json" \
+  -d '{"message": "查询贵州茅台的股票信息"}'
+```
+
+### 2. 前端部署
+
+```bash
+# 构建
+npm run build
+
+# 部署
+scp -r build/* user@server:/var/www/valuefrontier.cn/
+```
+
+### 3. 验证
+
+访问 https://valuefrontier.cn/agent-chat，测试对话：
+
+**测试用例**：
+1. "查询贵州茅台的股票信息" → 应返回自然语言总结
+2. "今日涨停的股票有哪些" → 应返回涨停股票列表并总结
+3. "新能源概念板块表现如何" → 应搜索概念并分析
+
+## 📊 对比总结
+
+| 特性 | 简单匹配 | 前端LLM | 后端LLM ⭐ |
+|------|---------|---------|-----------|
+| 实现难度 | 简单 | 中等 | 中等 |
+| 用户体验 | 差 | 好 | 好 |
+| 安全性 | 高 | 低 | 高 |
+| 成本 | 无 | 用户承担 | 服务器承担 |
+| 可维护性 | 差 | 中 | 好 |
+| **推荐指数** | ⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ |
+
+## 🎯 最终推荐
+
+**生产环境：后端集成 LLM (方案 B)**
+- 使用通义千问（qwen-plus）
+- 成本低（约 ¥50/月，10000次对话）
+- 安全可靠
+
+**快速原型：前端集成 LLM (方案 A)**
+- 适合演示
+- 快速验证可行性
+- 后续再迁移到后端
--- a/kimi_integration.py
+++ b/kimi_integration.py
@@ -0,0 +1,361 @@
+"""
+Kimi API 集成示例
+演示如何将MCP工具与Kimi大模型结合使用
+"""
+
+from openai import OpenAI
+import json
+from typing import List, Dict, Any
+from mcp_client_example import MCPClient
+
+# Kimi API配置
+KIMI_API_KEY = "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5"
+KIMI_BASE_URL = "https://api.moonshot.cn/v1"
+KIMI_MODEL = "kimi-k2-turbpreview"
+
+# 初始化Kimi客户端
+kimi_client = OpenAI(
+    api_key=KIMI_API_KEY,
+    base_url=KIMI_BASE_URL,
+)
+
+# 初始化MCP客户端
+mcp_client = MCPClient()
+
+
+def convert_mcp_tools_to_kimi_format() -> tuple[List[Dict], Dict]:
+    """
+    将MCP工具转换为Kimi API的tools格式
+
+    Returns:
+        tools: Kimi格式的工具列表
+        tool_map: 工具名称到执行函数的映射
+    """
+    # 获取所有MCP工具
+    mcp_tools_response = mcp_client.list_tools()
+    mcp_tools = mcp_tools_response["tools"]
+
+    # 转换为Kimi格式
+    kimi_tools = []
+    tool_map = {}
+
+    for tool in mcp_tools:
+        # Kimi工具格式
+        kimi_tool = {
+            "type": "function",
+            "function": {
+                "name": tool["name"],
+                "description": tool["description"],
+                "parameters": tool["parameters"]
+            }
+        }
+        kimi_tools.append(kimi_tool)
+
+        # 创建工具执行函数
+        tool_name = tool["name"]
+        tool_map[tool_name] = lambda args, name=tool_name: execute_mcp_tool(name, args)
+
+    return kimi_tools, tool_map
+
+
+def execute_mcp_tool(tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    执行MCP工具
+
+    Args:
+        tool_name: 工具名称
+        arguments: 工具参数
+
+    Returns:
+        工具执行结果
+    """
+    print(f"[工具调用] {tool_name}")
+    print(f"[参数] {json.dumps(arguments, ensure_ascii=False, indent=2)}")
+
+    result = mcp_client.call_tool(tool_name, arguments)
+
+    print(f"[结果] 成功: {result.get('success', False)}")
+
+    return result
+
+
+def chat_with_kimi(user_message: str, verbose: bool = True) -> str:
+    """
+    与Kimi进行对话，支持工具调用
+
+    Args:
+        user_message: 用户消息
+        verbose: 是否打印详细信息
+
+    Returns:
+        Kimi的回复
+    """
+    # 获取Kimi格式的工具
+    tools, tool_map = convert_mcp_tools_to_kimi_format()
+
+    if verbose:
+        print(f"\n{'='*60}")
+        print(f"加载了 {len(tools)} 个工具")
+        print(f"{'='*60}\n")
+
+    # 初始化对话
+    messages = [
+        {
+            "role": "system",
+            "content": """你是一个专业的金融数据分析助手，由 Moonshot AI 提供支持。
+你可以使用各种工具来帮助用户查询和分析金融数据，包括：
+- 新闻搜索（全球新闻、中国新闻、医疗新闻）
+- 公司研究（路演信息、研究报告）
+- 概念板块分析
+- 股票分析（涨停分析、财务数据、交易数据）
+- 财务报表（资产负债表、现金流量表）
+
+请根据用户的问题，选择合适的工具来获取信息，并提供专业的分析和建议。"""
+        },
+        {
+            "role": "user",
+            "content": user_message
+        }
+    ]
+
+    if verbose:
+        print(f"[用户]: {user_message}\n")
+
+    # 对话循环，处理工具调用
+    finish_reason = None
+    iteration = 0
+    max_iterations = 10  # 防止无限循环
+
+    while finish_reason is None or finish_reason == "tool_calls":
+        iteration += 1
+        if iteration > max_iterations:
+            print("[警告] 达到最大迭代次数")
+            break
+
+        if verbose and iteration > 1:
+            print(f"\n[轮次 {iteration}]")
+
+        # 调用Kimi API
+        completion = kimi_client.chat.completions.create(
+            model=KIMI_MODEL,
+            messages=messages,
+            temperature=0.6,  # Kimi推荐的temperature值
+            tools=tools,
+        )
+
+        choice = completion.choices[0]
+        finish_reason = choice.finish_reason
+
+        if verbose:
+            print(f"[Kimi] finish_reason: {finish_reason}")
+
+        # 处理工具调用
+        if finish_reason == "tool_calls":
+            # 将Kimi的消息添加到上下文
+            messages.append(choice.message)
+
+            # 执行每个工具调用
+            for tool_call in choice.message.tool_calls:
+                tool_name = tool_call.function.name
+                tool_arguments = json.loads(tool_call.function.arguments)
+
+                # 执行工具
+                tool_result = tool_map[tool_name](tool_arguments)
+
+                # 将工具结果添加到消息中
+                messages.append({
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "name": tool_name,
+                    "content": json.dumps(tool_result, ensure_ascii=False),
+                })
+
+            if verbose:
+                print()  # 空行分隔
+
+    # 返回最终回复
+    final_response = choice.message.content
+
+    if verbose:
+        print(f"\n[Kimi]: {final_response}\n")
+        print(f"{'='*60}")
+
+    return final_response
+
+
+def demo_simple_query():
+    """演示1: 简单查询"""
+    print("\n" + "="*60)
+    print("演示1: 简单新闻查询")
+    print("="*60)
+
+    response = chat_with_kimi("帮我查找关于人工智能的最新新闻")
+    return response
+
+
+def demo_stock_analysis():
+    """演示2: 股票分析"""
+    print("\n" + "="*60)
+    print("演示2: 股票财务分析")
+    print("="*60)
+
+    response = chat_with_kimi("帮我分析贵州茅台（600519）的财务状况")
+    return response
+
+
+def demo_concept_research():
+    """演示3: 概念研究"""
+    print("\n" + "="*60)
+    print("演示3: 概念板块研究")
+    print("="*60)
+
+    response = chat_with_kimi("查找新能源汽车相关的概念板块，并告诉我涨幅最高的是哪些")
+    return response
+
+
+def demo_industry_comparison():
+    """演示4: 行业对比"""
+    print("\n" + "="*60)
+    print("演示4: 行业内股票对比")
+    print("="*60)
+
+    response = chat_with_kimi("帮我找出半导体行业的龙头股票，并对比它们的财务指标")
+    return response
+
+
+def demo_comprehensive_analysis():
+    """演示5: 综合分析"""
+    print("\n" + "="*60)
+    print("演示5: 综合分析")
+    print("="*60)
+
+    response = chat_with_kimi("""
+    我想投资白酒行业，请帮我：
+    1. 搜索白酒行业的主要上市公司
+    2. 对比贵州茅台和五粮液的财务数据
+    3. 查看最近的行业新闻
+    4. 给出投资建议
+    """)
+    return response
+
+
+def interactive_chat():
+    """交互式对话"""
+    print("\n" + "="*60)
+    print("Kimi 金融助手 - 交互模式")
+    print("="*60)
+    print("提示：输入 'quit' 或 'exit' 退出")
+    print("="*60 + "\n")
+
+    while True:
+        try:
+            user_input = input("你: ").strip()
+
+            if not user_input:
+                continue
+
+            if user_input.lower() in ['quit', 'exit', '退出']:
+                print("\n再见！")
+                break
+
+            response = chat_with_kimi(user_input)
+
+        except KeyboardInterrupt:
+            print("\n\n再见！")
+            break
+        except Exception as e:
+            print(f"\n[错误] {str(e)}\n")
+
+
+def test_kimi_connection():
+    """测试Kimi API连接"""
+    print("\n" + "="*60)
+    print("测试 Kimi API 连接")
+    print("="*60 + "\n")
+
+    try:
+        # 简单的测试请求
+        response = kimi_client.chat.completions.create(
+            model=KIMI_MODEL,
+            messages=[
+                {"role": "user", "content": "你好，请介绍一下你自己"}
+            ],
+            temperature=0.6
+        )
+
+        print("[✓] 连接成功!")
+        print(f"[✓] 模型: {KIMI_MODEL}")
+        print(f"[✓] 回复: {response.choices[0].message.content}\n")
+
+        return True
+    except Exception as e:
+        print(f"[✗] 连接失败: {str(e)}\n")
+        return False
+
+
+def show_available_tools():
+    """显示所有可用工具"""
+    print("\n" + "="*60)
+    print("可用工具列表")
+    print("="*60 + "\n")
+
+    tools, _ = convert_mcp_tools_to_kimi_format()
+
+    for i, tool in enumerate(tools, 1):
+        func = tool["function"]
+        print(f"{i}. {func['name']}")
+        print(f"   描述: {func['description'][:80]}...")
+        print()
+
+    print(f"总计: {len(tools)} 个工具\n")
+
+
+if __name__ == "__main__":
+    import sys
+
+    # 首先测试连接
+    if not test_kimi_connection():
+        print("请检查API Key和网络连接")
+        sys.exit(1)
+
+    # 显示可用工具
+    show_available_tools()
+
+    # 运行演示
+    print("\n选择运行模式:")
+    print("1. 简单查询演示")
+    print("2. 股票分析演示")
+    print("3. 概念研究演示")
+    print("4. 行业对比演示")
+    print("5. 综合分析演示")
+    print("6. 交互式对话")
+    print("7. 运行所有演示")
+
+    try:
+        choice = input("\n请选择 (1-7): ").strip()
+
+        if choice == "1":
+            demo_simple_query()
+        elif choice == "2":
+            demo_stock_analysis()
+        elif choice == "3":
+            demo_concept_research()
+        elif choice == "4":
+            demo_industry_comparison()
+        elif choice == "5":
+            demo_comprehensive_analysis()
+        elif choice == "6":
+            interactive_chat()
+        elif choice == "7":
+            demo_simple_query()
+            demo_stock_analysis()
+            demo_concept_research()
+            demo_industry_comparison()
+            demo_comprehensive_analysis()
+        else:
+            print("无效选择")
+
+    except KeyboardInterrupt:
+        print("\n\n程序已退出")
+    finally:
+        mcp_client.close()
--- a/mcp_agent_system.py
+++ b/mcp_agent_system.py
@@ -0,0 +1,470 @@
+"""
+MCP Agent System - 基于 DeepResearch 逻辑的智能代理系统
+三阶段流程：计划制定 → 工具执行 → 结果总结
+"""
+
+from pydantic import BaseModel
+from typing import List, Dict, Any, Optional, Literal
+from datetime import datetime
+import json
+import logging
+from openai import OpenAI
+import asyncio
+import os
+
+logger = logging.getLogger(__name__)
+
+# ==================== 数据模型 ====================
+
+class ToolCall(BaseModel):
+    """工具调用"""
+    tool: str
+    arguments: Dict[str, Any]
+    reason: str  # 为什么要调用这个工具
+
+class ExecutionPlan(BaseModel):
+    """执行计划"""
+    goal: str  # 用户的目标
+    steps: List[ToolCall]  # 执行步骤
+    reasoning: str  # 规划reasoning
+
+class StepResult(BaseModel):
+    """单步执行结果"""
+    step_index: int
+    tool: str
+    arguments: Dict[str, Any]
+    status: Literal["success", "failed", "skipped"]
+    result: Optional[Any] = None
+    error: Optional[str] = None
+    execution_time: float = 0
+
+class AgentResponse(BaseModel):
+    """Agent响应"""
+    success: bool
+    message: str  # 自然语言总结
+    plan: Optional[ExecutionPlan] = None  # 执行计划
+    step_results: List[StepResult] = []  # 每步的结果
+    final_summary: Optional[str] = None  # 最终总结
+    metadata: Optional[Dict[str, Any]] = None
+
+class ChatRequest(BaseModel):
+    """聊天请求"""
+    message: str
+    conversation_history: List[Dict[str, str]] = []
+    stream: bool = False  # 是否流式输出
+
+# ==================== Agent 系统 ====================
+
+class MCPAgent:
+    """MCP 智能代理 - 三阶段执行"""
+
+    def __init__(self, provider: str = "qwen"):
+        self.provider = provider
+
+        # LLM 配置
+        config = {
+            "qwen": {
+                "api_key": os.getenv("DASHSCOPE_API_KEY", ""),
+                "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+                "model": "qwen-plus",
+            },
+            "deepseek": {
+                "api_key": os.getenv("DEEPSEEK_API_KEY", ""),
+                "base_url": "https://api.deepseek.com/v1",
+                "model": "deepseek-chat",
+            },
+            "openai": {
+                "api_key": os.getenv("OPENAI_API_KEY", ""),
+                "base_url": "https://api.openai.com/v1",
+                "model": "gpt-4o-mini",
+            },
+        }.get(provider)
+
+        if not config or not config["api_key"]:
+            raise ValueError(f"Provider '{provider}' not configured. Please set API key.")
+
+        self.client = OpenAI(
+            api_key=config["api_key"],
+            base_url=config["base_url"],
+        )
+        self.model = config["model"]
+
+    # ==================== 阶段 1: 计划制定 ====================
+
+    def get_planning_prompt(self, tools: List[dict]) -> str:
+        """获取计划制定的系统提示词"""
+        tools_desc = "\n\n".join([
+            f"**{tool['name']}**\n"
+            f"描述：{tool['description']}\n"
+            f"参数：{json.dumps(tool['parameters'], ensure_ascii=False, indent=2)}"
+            for tool in tools
+        ])
+
+        return f"""你是一个专业的金融研究助手。你需要根据用户的问题，制定一个详细的执行计划。
+
+## 可用工具
+
+{tools_desc}
+
+## 重要知识
+- 贵州茅台股票代码: 600519
+- 涨停: 股价单日涨幅约10%
+- 概念板块: 相同题材的股票分类
+
+## 特殊工具说明
+- **summarize_with_llm**: 这是一个特殊工具，用于让你总结和分析收集到的数据
+  - 当需要对多个数据源进行综合分析时使用
+  - 当需要生成研究报告时使用
+  - 参数: {{"data": "要分析的数据", "task": "分析任务描述"}}
+
+## 任务
+分析用户问题，制定执行计划。返回 JSON 格式：
+
+```json
+{{
+  "goal": "用户的目标（一句话概括）",
+  "reasoning": "你的分析思路（为什么这样规划）",
+  "steps": [
+    {{
+      "tool": "工具名称",
+      "arguments": {{"参数名": "参数值"}},
+      "reason": "为什么要执行这一步"
+    }}
+  ]
+}}
+```
+
+## 规划原则
+1. **从简到繁**: 先获取基础信息，再深入分析
+2. **数据先行**: 先收集数据，再总结分析
+3. **合理组合**: 可以调用多个工具，但不要超过5个
+4. **包含总结**: 最后一步通常是 summarize_with_llm
+
+## 示例
+
+用户："帮我全面分析一下贵州茅台这只股票"
+
+你的计划：
+```json
+{{
+  "goal": "全面分析贵州茅台股票",
+  "reasoning": "需要获取基本信息、财务指标、交易数据，然后综合分析",
+  "steps": [
+    {{
+      "tool": "get_stock_basic_info",
+      "arguments": {{"seccode": "600519"}},
+      "reason": "获取股票基本信息（公司名称、行业、市值等）"
+    }},
+    {{
+      "tool": "get_stock_financial_index",
+      "arguments": {{"seccode": "600519", "limit": 5}},
+      "reason": "获取最近5期财务指标（营收、利润、ROE等）"
+    }},
+    {{
+      "tool": "get_stock_trade_data",
+      "arguments": {{"seccode": "600519", "limit": 30}},
+      "reason": "获取最近30天交易数据（价格走势、成交量）"
+    }},
+    {{
+      "tool": "search_china_news",
+      "arguments": {{"query": "贵州茅台", "top_k": 5}},
+      "reason": "获取最新新闻，了解市场动态"
+    }},
+    {{
+      "tool": "summarize_with_llm",
+      "arguments": {{
+        "data": "前面收集的所有数据",
+        "task": "综合分析贵州茅台的投资价值，包括基本面、财务状况、股价走势、市场情绪"
+      }},
+      "reason": "综合所有数据，生成投资分析报告"
+    }}
+  ]
+}}
+```
+
+只返回JSON，不要额外解释。"""
+
+    async def create_plan(self, user_query: str, tools: List[dict]) -> ExecutionPlan:
+        """阶段1: 创建执行计划"""
+        logger.info(f"[Planning] Creating plan for: {user_query}")
+
+        messages = [
+            {"role": "system", "content": self.get_planning_prompt(tools)},
+            {"role": "user", "content": user_query},
+        ]
+
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=0.3,
+            max_tokens=1500,
+        )
+
+        plan_json = response.choices[0].message.content.strip()
+        logger.info(f"[Planning] Raw response: {plan_json}")
+
+        # 清理可能的代码块标记
+        if "```json" in plan_json:
+            plan_json = plan_json.split("```json")[1].split("```")[0].strip()
+        elif "```" in plan_json:
+            plan_json = plan_json.split("```")[1].split("```")[0].strip()
+
+        plan_data = json.loads(plan_json)
+
+        plan = ExecutionPlan(
+            goal=plan_data["goal"],
+            reasoning=plan_data.get("reasoning", ""),
+            steps=[
+                ToolCall(**step) for step in plan_data["steps"]
+            ],
+        )
+
+        logger.info(f"[Planning] Plan created: {len(plan.steps)} steps")
+        return plan
+
+    # ==================== 阶段 2: 工具执行 ====================
+
+    async def execute_tool(
+        self,
+        tool_name: str,
+        arguments: Dict[str, Any],
+        tool_handlers: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """执行单个工具"""
+
+        # 特殊处理：summarize_with_llm
+        if tool_name == "summarize_with_llm":
+            return await self.summarize_with_llm(
+                data=arguments.get("data", ""),
+                task=arguments.get("task", "总结数据"),
+            )
+
+        # 调用 MCP 工具
+        handler = tool_handlers.get(tool_name)
+        if not handler:
+            raise ValueError(f"Tool '{tool_name}' not found")
+
+        result = await handler(arguments)
+        return result
+
+    async def execute_plan(
+        self,
+        plan: ExecutionPlan,
+        tool_handlers: Dict[str, Any],
+    ) -> List[StepResult]:
+        """阶段2: 执行计划中的所有步骤"""
+        logger.info(f"[Execution] Starting execution: {len(plan.steps)} steps")
+
+        results = []
+        collected_data = {}  # 收集的数据，供后续步骤使用
+
+        for i, step in enumerate(plan.steps):
+            logger.info(f"[Execution] Step {i+1}/{len(plan.steps)}: {step.tool}")
+
+            start_time = datetime.now()
+
+            try:
+                # 替换 arguments 中的占位符
+                arguments = step.arguments.copy()
+                if step.tool == "summarize_with_llm" and arguments.get("data") == "前面收集的所有数据":
+                    # 将收集的数据传递给总结工具
+                    arguments["data"] = json.dumps(collected_data, ensure_ascii=False, indent=2)
+
+                # 执行工具
+                result = await self.execute_tool(step.tool, arguments, tool_handlers)
+
+                execution_time = (datetime.now() - start_time).total_seconds()
+
+                # 保存结果
+                step_result = StepResult(
+                    step_index=i,
+                    tool=step.tool,
+                    arguments=arguments,
+                    status="success",
+                    result=result,
+                    execution_time=execution_time,
+                )
+                results.append(step_result)
+
+                # 收集数据
+                collected_data[f"step_{i+1}_{step.tool}"] = result
+
+                logger.info(f"[Execution] Step {i+1} completed in {execution_time:.2f}s")
+
+            except Exception as e:
+                logger.error(f"[Execution] Step {i+1} failed: {str(e)}")
+
+                execution_time = (datetime.now() - start_time).total_seconds()
+
+                step_result = StepResult(
+                    step_index=i,
+                    tool=step.tool,
+                    arguments=step.arguments,
+                    status="failed",
+                    error=str(e),
+                    execution_time=execution_time,
+                )
+                results.append(step_result)
+
+                # 根据错误类型决定是否继续
+                if "not found" in str(e).lower():
+                    logger.warning(f"[Execution] Stopping due to critical error")
+                    break
+                else:
+                    logger.warning(f"[Execution] Continuing despite error")
+                    continue
+
+        logger.info(f"[Execution] Execution completed: {len(results)} steps")
+        return results
+
+    async def summarize_with_llm(self, data: str, task: str) -> str:
+        """特殊工具：使用 LLM 总结数据"""
+        logger.info(f"[LLM Summary] Task: {task}")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "你是一个专业的金融分析师。根据提供的数据，完成指定的分析任务。"
+            },
+            {
+                "role": "user",
+                "content": f"## 任务\n{task}\n\n## 数据\n{data}\n\n请根据数据完成分析任务，用专业且易懂的语言呈现。"
+            },
+        ]
+
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=0.7,
+            max_tokens=2000,
+        )
+
+        summary = response.choices[0].message.content
+        return summary
+
+    # ==================== 阶段 3: 结果总结 ====================
+
+    async def generate_final_summary(
+        self,
+        user_query: str,
+        plan: ExecutionPlan,
+        step_results: List[StepResult],
+    ) -> str:
+        """阶段3: 生成最终总结"""
+        logger.info("[Summary] Generating final summary")
+
+        # 收集所有成功的结果
+        successful_results = [r for r in step_results if r.status == "success"]
+
+        if not successful_results:
+            return "很抱歉，所有步骤都执行失败，无法生成分析报告。"
+
+        # 构建总结提示
+        results_text = "\n\n".join([
+            f"**步骤 {r.step_index + 1}: {r.tool}**\n"
+            f"结果: {json.dumps(r.result, ensure_ascii=False, indent=2)[:1000]}..."
+            for r in successful_results
+        ])
+
+        messages = [
+            {
+                "role": "system",
+                "content": "你是一个专业的金融研究助手。根据执行结果，生成一份简洁清晰的报告。"
+            },
+            {
+                "role": "user",
+                "content": f"""
+用户问题：{user_query}
+
+执行计划：{plan.goal}
+
+执行结果：
+{results_text}
+
+请根据以上信息，生成一份专业的分析报告（300字以内）。
+"""
+            },
+        ]
+
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=messages,
+            temperature=0.7,
+            max_tokens=1000,
+        )
+
+        summary = response.choices[0].message.content
+        logger.info("[Summary] Final summary generated")
+        return summary
+
+    # ==================== 主流程 ====================
+
+    async def process_query(
+        self,
+        user_query: str,
+        tools: List[dict],
+        tool_handlers: Dict[str, Any],
+    ) -> AgentResponse:
+        """主流程：处理用户查询"""
+        logger.info(f"[Agent] Processing query: {user_query}")
+
+        try:
+            # 阶段 1: 创建计划
+            plan = await self.create_plan(user_query, tools)
+
+            # 阶段 2: 执行计划
+            step_results = await self.execute_plan(plan, tool_handlers)
+
+            # 阶段 3: 生成总结
+            final_summary = await self.generate_final_summary(
+                user_query, plan, step_results
+            )
+
+            return AgentResponse(
+                success=True,
+                message=final_summary,
+                plan=plan,
+                step_results=step_results,
+                final_summary=final_summary,
+                metadata={
+                    "total_steps": len(plan.steps),
+                    "successful_steps": len([r for r in step_results if r.status == "success"]),
+                    "failed_steps": len([r for r in step_results if r.status == "failed"]),
+                    "total_execution_time": sum(r.execution_time for r in step_results),
+                },
+            )
+
+        except Exception as e:
+            logger.error(f"[Agent] Error: {str(e)}", exc_info=True)
+            return AgentResponse(
+                success=False,
+                message=f"处理失败: {str(e)}",
+            )
+
+# ==================== FastAPI 端点 ====================
+
+"""
+在 mcp_server.py 中添加:
+
+from mcp_agent_system import MCPAgent, ChatRequest, AgentResponse
+
+# 创建 Agent 实例
+agent = MCPAgent(provider="qwen")
+
+@app.post("/agent/chat", response_model=AgentResponse)
+async def agent_chat(request: ChatRequest):
+    \"\"\"智能代理对话端点\"\"\"
+    logger.info(f"Agent chat: {request.message}")
+
+    # 获取工具列表和处理器
+    tools = [tool.dict() for tool in TOOLS]
+
+    # 处理查询
+    response = await agent.process_query(
+        user_query=request.message,
+        tools=tools,
+        tool_handlers=TOOL_HANDLERS,
+    )
+
+    return response
+"""
--- a/mcp_chat_endpoint.py
+++ b/mcp_chat_endpoint.py
@@ -0,0 +1,295 @@
+"""
+MCP Chat Endpoint - 添加到 mcp_server.py
+集成LLM实现智能对话，自动调用MCP工具并总结结果
+"""
+
+from pydantic import BaseModel
+from typing import List, Dict, Any, Optional
+import os
+import json
+from openai import OpenAI
+import logging
+
+logger = logging.getLogger(__name__)
+
+# ==================== LLM配置 ====================
+
+# 支持多种LLM提供商
+LLM_PROVIDERS = {
+    "openai": {
+        "api_key": os.getenv("OPENAI_API_KEY", ""),
+        "base_url": "https://api.openai.com/v1",
+        "model": "gpt-4o-mini",  # 便宜且快速
+    },
+    "qwen": {
+        "api_key": os.getenv("DASHSCOPE_API_KEY", ""),
+        "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+        "model": "qwen-plus",
+    },
+    "deepseek": {
+        "api_key": os.getenv("DEEPSEEK_API_KEY", ""),
+        "base_url": "https://api.deepseek.com/v1",
+        "model": "deepseek-chat",
+    },
+}
+
+# 默认使用的LLM提供商
+DEFAULT_PROVIDER = "qwen"  # 推荐使用通义千问，价格便宜
+
+# ==================== 数据模型 ====================
+
+class Message(BaseModel):
+    """消息"""
+    role: str  # system, user, assistant
+    content: str
+
+class ChatRequest(BaseModel):
+    """聊天请求"""
+    message: str
+    conversation_history: List[Dict[str, str]] = []
+    provider: Optional[str] = DEFAULT_PROVIDER
+
+class ChatResponse(BaseModel):
+    """聊天响应"""
+    success: bool
+    message: str
+    tool_used: Optional[str] = None
+    raw_data: Optional[Any] = None
+    error: Optional[str] = None
+
+# ==================== LLM助手类 ====================
+
+class MCPChatAssistant:
+    """MCP聊天助手 - 集成LLM和工具调用"""
+
+    def __init__(self, provider: str = DEFAULT_PROVIDER):
+        self.provider = provider
+        config = LLM_PROVIDERS.get(provider)
+
+        if not config or not config["api_key"]:
+            logger.warning(f"LLM provider '{provider}' not configured, using fallback mode")
+            self.client = None
+        else:
+            self.client = OpenAI(
+                api_key=config["api_key"],
+                base_url=config["base_url"],
+            )
+            self.model = config["model"]
+
+    def get_system_prompt(self, tools: List[dict]) -> str:
+        """构建系统提示词"""
+        tools_desc = "\n\n".join([
+            f"**{tool['name']}**\n描述：{tool['description']}\n参数：{json.dumps(tool['parameters'], ensure_ascii=False, indent=2)}"
+            for tool in tools
+        ])
+
+        return f"""你是一个专业的金融投资助手。你可以使用以下工具来帮助用户查询信息：
+
+{tools_desc}
+
+## 工作流程
+1. **理解用户意图**：分析用户问题，确定需要什么信息
+2. **选择工具**：从上面的工具中选择最合适的一个或多个
+3. **提取参数**：从用户输入中提取工具需要的参数
+4. **返回工具调用指令**（JSON格式）：
+   {{"tool": "工具名", "arguments": {{...}}}}
+
+## 重要规则
+- 贵州茅台的股票代码是 **600519**
+- 如果用户提到股票名称，尝试推断股票代码
+- 如果不确定需要什么信息，使用 search_china_news 搜索相关新闻
+- 涨停是指股票当日涨幅达到10%左右
+- 只返回工具调用指令，不要额外解释
+
+## 示例
+用户："查询贵州茅台的股票信息"
+你：{{"tool": "get_stock_basic_info", "arguments": {{"seccode": "600519"}}}}
+
+用户："今日涨停的股票有哪些"
+你：{{"tool": "search_limit_up_stocks", "arguments": {{"query": "", "mode": "hybrid", "page_size": 10}}}}
+
+用户："新能源概念板块表现如何"
+你：{{"tool": "search_concepts", "arguments": {{"query": "新能源", "size": 10, "sort_by": "change_pct"}}}}
+"""
+
+    async def chat(self, user_message: str, conversation_history: List[Dict[str, str]], tools: List[dict]) -> ChatResponse:
+        """智能对话"""
+        try:
+            if not self.client:
+                # 降级到简单匹配
+                return await self.fallback_chat(user_message)
+
+            # 1. 构建消息历史
+            messages = [
+                {"role": "system", "content": self.get_system_prompt(tools)},
+            ]
+
+            # 添加历史对话（最多保留最近10轮）
+            for msg in conversation_history[-20:]:
+                messages.append({
+                    "role": "user" if msg.get("isUser") else "assistant",
+                    "content": msg.get("content", ""),
+                })
+
+            messages.append({"role": "user", "content": user_message})
+
+            # 2. 调用LLM获取工具调用指令
+            logger.info(f"Calling LLM with {len(messages)} messages")
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                temperature=0.3,  # 低温度，更确定性
+                max_tokens=500,
+            )
+
+            tool_call_instruction = response.choices[0].message.content.strip()
+            logger.info(f"LLM response: {tool_call_instruction}")
+
+            # 3. 解析工具调用指令
+            try:
+                tool_call = json.loads(tool_call_instruction)
+                tool_name = tool_call.get("tool")
+                tool_args = tool_call.get("arguments", {})
+
+                if not tool_name:
+                    raise ValueError("No tool specified")
+
+                # 4. 调用工具（这里需要导入 mcp_server 的工具处理器）
+                from mcp_server import TOOL_HANDLERS
+
+                handler = TOOL_HANDLERS.get(tool_name)
+                if not handler:
+                    raise ValueError(f"Tool '{tool_name}' not found")
+
+                tool_result = await handler(tool_args)
+
+                # 5. 让LLM总结结果
+                summary_messages = messages + [
+                    {"role": "assistant", "content": tool_call_instruction},
+                    {"role": "system", "content": f"工具 {tool_name} 返回的数据：\n{json.dumps(tool_result, ensure_ascii=False, indent=2)}\n\n请用自然语言总结这些数据，给用户一个简洁清晰的回复（不超过200字）。"}
+                ]
+
+                summary_response = self.client.chat.completions.create(
+                    model=self.model,
+                    messages=summary_messages,
+                    temperature=0.7,
+                    max_tokens=300,
+                )
+
+                summary = summary_response.choices[0].message.content
+
+                return ChatResponse(
+                    success=True,
+                    message=summary,
+                    tool_used=tool_name,
+                    raw_data=tool_result,
+                )
+
+            except json.JSONDecodeError:
+                # LLM没有返回JSON格式，直接返回其回复
+                return ChatResponse(
+                    success=True,
+                    message=tool_call_instruction,
+                )
+            except Exception as tool_error:
+                logger.error(f"Tool execution error: {str(tool_error)}")
+                return ChatResponse(
+                    success=False,
+                    message="工具调用失败",
+                    error=str(tool_error),
+                )
+
+        except Exception as e:
+            logger.error(f"Chat error: {str(e)}", exc_info=True)
+            return ChatResponse(
+                success=False,
+                message="对话处理失败",
+                error=str(e),
+            )
+
+    async def fallback_chat(self, user_message: str) -> ChatResponse:
+        """降级方案：简单关键词匹配"""
+        from mcp_server import TOOL_HANDLERS
+
+        try:
+            # 茅台特殊处理
+            if "茅台" in user_message or "贵州茅台" in user_message:
+                handler = TOOL_HANDLERS.get("get_stock_basic_info")
+                result = await handler({"seccode": "600519"})
+                return ChatResponse(
+                    success=True,
+                    message="已为您查询贵州茅台(600519)的股票信息：",
+                    tool_used="get_stock_basic_info",
+                    raw_data=result,
+                )
+
+            # 涨停分析
+            elif "涨停" in user_message:
+                handler = TOOL_HANDLERS.get("search_limit_up_stocks")
+                query = user_message.replace("涨停", "").strip()
+                result = await handler({"query": query, "mode": "hybrid", "page_size": 10})
+                return ChatResponse(
+                    success=True,
+                    message="已为您查询涨停股票信息：",
+                    tool_used="search_limit_up_stocks",
+                    raw_data=result,
+                )
+
+            # 概念板块
+            elif "概念" in user_message or "板块" in user_message:
+                handler = TOOL_HANDLERS.get("search_concepts")
+                query = user_message.replace("概念", "").replace("板块", "").strip()
+                result = await handler({"query": query, "size": 10, "sort_by": "change_pct"})
+                return ChatResponse(
+                    success=True,
+                    message=f"已为您查询'{query}'相关概念板块：",
+                    tool_used="search_concepts",
+                    raw_data=result,
+                )
+
+            # 默认：搜索新闻
+            else:
+                handler = TOOL_HANDLERS.get("search_china_news")
+                result = await handler({"query": user_message, "top_k": 5})
+                return ChatResponse(
+                    success=True,
+                    message="已为您搜索相关新闻：",
+                    tool_used="search_china_news",
+                    raw_data=result,
+                )
+
+        except Exception as e:
+            logger.error(f"Fallback chat error: {str(e)}")
+            return ChatResponse(
+                success=False,
+                message="查询失败",
+                error=str(e),
+            )
+
+# ==================== FastAPI端点 ====================
+
+# 在 mcp_server.py 中添加以下代码：
+
+"""
+from mcp_chat_endpoint import MCPChatAssistant, ChatRequest, ChatResponse
+
+# 创建聊天助手实例
+chat_assistant = MCPChatAssistant(provider="qwen")  # 或 "openai", "deepseek"
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    \"\"\"智能对话端点 - 使用LLM理解意图并调用工具\"\"\"
+    logger.info(f"Chat request: {request.message}")
+
+    # 获取可用工具列表
+    tools = [tool.dict() for tool in TOOLS]
+
+    # 调用聊天助手
+    response = await chat_assistant.chat(
+        user_message=request.message,
+        conversation_history=request.conversation_history,
+        tools=tools,
+    )
+
+    return response
+"""
--- a/mcp_client_example.py
+++ b/mcp_client_example.py
@@ -0,0 +1,248 @@
+"""
+MCP客户端使用示例
+演示如何调用MCP服务器的各种工具
+"""
+
+import httpx
+import json
+from typing import Dict, Any
+
+
+class MCPClient:
+    """MCP客户端"""
+
+    def __init__(self, base_url: str = "http://localhost:8900"):
+        self.base_url = base_url
+        self.client = httpx.Client(timeout=60.0)
+
+    def list_tools(self):
+        """列出所有可用工具"""
+        response = self.client.get(f"{self.base_url}/tools")
+        response.raise_for_status()
+        return response.json()
+
+    def get_tool(self, tool_name: str):
+        """获取特定工具的定义"""
+        response = self.client.get(f"{self.base_url}/tools/{tool_name}")
+        response.raise_for_status()
+        return response.json()
+
+    def call_tool(self, tool_name: str, arguments: Dict[str, Any]):
+        """调用工具"""
+        payload = {
+            "tool": tool_name,
+            "arguments": arguments
+        }
+        response = self.client.post(f"{self.base_url}/tools/call", json=payload)
+        response.raise_for_status()
+        return response.json()
+
+    def close(self):
+        """关闭客户端"""
+        self.client.close()
+
+
+def print_result(title: str, result: Dict[str, Any]):
+    """打印结果"""
+    print(f"\n{'=' * 60}")
+    print(f"{title}")
+    print(f"{'=' * 60}")
+    print(json.dumps(result, ensure_ascii=False, indent=2))
+
+
+def main():
+    """主函数 - 演示各种工具的使用"""
+
+    client = MCPClient()
+
+    try:
+        # 1. 列出所有工具
+        print("\n示例1: 列出所有可用工具")
+        tools = client.list_tools()
+        print(f"可用工具数量: {len(tools['tools'])}")
+        for tool in tools['tools']:
+            print(f"  - {tool['name']}: {tool['description'][:50]}...")
+
+        # 2. 搜索中国新闻
+        print("\n示例2: 搜索中国新闻（关键词：人工智能）")
+        result = client.call_tool(
+            "search_china_news",
+            {
+                "query": "人工智能",
+                "top_k": 5
+            }
+        )
+        if result['success']:
+            print_result("中国新闻搜索结果", result['data'])
+
+        # 3. 搜索概念板块（按涨跌幅排序）
+        print("\n示例3: 搜索概念板块（关键词：新能源，按涨跌幅排序）")
+        result = client.call_tool(
+            "search_concepts",
+            {
+                "query": "新能源",
+                "size": 5,
+                "sort_by": "change_pct"
+            }
+        )
+        if result['success']:
+            print_result("概念搜索结果", result['data'])
+
+        # 4. 获取股票的相关概念
+        print("\n示例4: 获取股票相关概念（股票代码：600519）")
+        result = client.call_tool(
+            "get_stock_concepts",
+            {
+                "stock_code": "600519",
+                "size": 10
+            }
+        )
+        if result['success']:
+            print_result("股票概念结果", result['data'])
+
+        # 5. 搜索涨停股票
+        print("\n示例5: 搜索涨停股票（关键词：锂电池）")
+        result = client.call_tool(
+            "search_limit_up_stocks",
+            {
+                "query": "锂电池",
+                "mode": "hybrid",
+                "page_size": 5
+            }
+        )
+        if result['success']:
+            print_result("涨停股票搜索结果", result['data'])
+
+        # 6. 搜索研究报告
+        print("\n示例6: 搜索研究报告（关键词：投资策略）")
+        result = client.call_tool(
+            "search_research_reports",
+            {
+                "query": "投资策略",
+                "mode": "hybrid",
+                "size": 3
+            }
+        )
+        if result['success']:
+            print_result("研究报告搜索结果", result['data'])
+
+        # 7. 获取概念统计数据
+        print("\n示例7: 获取概念统计（最近7天）")
+        result = client.call_tool(
+            "get_concept_statistics",
+            {
+                "days": 7,
+                "min_stock_count": 3
+            }
+        )
+        if result['success']:
+            print_result("概念统计结果", result['data'])
+
+        # 8. 搜索路演信息
+        print("\n示例8: 搜索路演信息（关键词：业绩）")
+        result = client.call_tool(
+            "search_roadshows",
+            {
+                "query": "业绩",
+                "size": 3
+            }
+        )
+        if result['success']:
+            print_result("路演搜索结果", result['data'])
+
+        # 9. 获取股票基本信息
+        print("\n示例9: 获取股票基本信息（股票：600519）")
+        result = client.call_tool(
+            "get_stock_basic_info",
+            {
+                "seccode": "600519"
+            }
+        )
+        if result['success']:
+            print_result("股票基本信息", result['data'])
+
+        # 10. 获取股票财务指标
+        print("\n示例10: 获取股票财务指标（股票：600519，最近5期）")
+        result = client.call_tool(
+            "get_stock_financial_index",
+            {
+                "seccode": "600519",
+                "limit": 5
+            }
+        )
+        if result['success']:
+            print_result("财务指标", result['data'])
+
+        # 11. 获取股票交易数据
+        print("\n示例11: 获取股票交易数据（股票：600519，最近10天）")
+        result = client.call_tool(
+            "get_stock_trade_data",
+            {
+                "seccode": "600519",
+                "limit": 10
+            }
+        )
+        if result['success']:
+            print_result("交易数据", result['data'])
+
+        # 12. 按行业搜索股票
+        print("\n示例12: 按行业搜索股票（行业：半导体）")
+        result = client.call_tool(
+            "search_stocks_by_criteria",
+            {
+                "industry": "半导体",
+                "limit": 10
+            }
+        )
+        if result['success']:
+            print_result("行业股票", result['data'])
+
+        # 13. 股票对比分析
+        print("\n示例13: 股票对比分析（600519 vs 000858）")
+        result = client.call_tool(
+            "get_stock_comparison",
+            {
+                "seccodes": ["600519", "000858"],
+                "metric": "financial"
+            }
+        )
+        if result['success']:
+            print_result("股票对比", result['data'])
+
+    except Exception as e:
+        print(f"\n错误: {str(e)}")
+
+    finally:
+        client.close()
+
+
+def test_single_tool():
+    """测试单个工具（用于快速测试）"""
+    client = MCPClient()
+
+    try:
+        # 修改这里来测试不同的工具
+        result = client.call_tool(
+            "search_china_news",
+            {
+                "query": "芯片",
+                "exact_match": True,
+                "top_k": 3
+            }
+        )
+
+        print_result("测试结果", result)
+
+    except Exception as e:
+        print(f"错误: {str(e)}")
+
+    finally:
+        client.close()
+
+
+if __name__ == "__main__":
+    # 运行完整示例
+    main()
+
+    # 或者测试单个工具
+    # test_single_tool()
--- a/mcp_config.py
+++ b/mcp_config.py
@@ -0,0 +1,108 @@
+"""
+MCP服务器配置文件
+集中管理所有配置项
+"""
+
+from typing import Dict
+from pydantic import BaseSettings
+
+class Settings(BaseSettings):
+    """应用配置"""
+
+    # 服务器配置
+    SERVER_HOST: str = "0.0.0.0"
+    SERVER_PORT: int = 8900
+    DEBUG: bool = True
+
+    # 后端API服务端点
+    NEWS_API_URL: str = "http://222.128.1.157:21891"
+    ROADSHOW_API_URL: str = "http://222.128.1.157:19800"
+    CONCEPT_API_URL: str = "http://222.128.1.157:16801"
+    STOCK_ANALYSIS_API_URL: str = "http://222.128.1.157:8811"
+
+    # HTTP客户端配置
+    HTTP_TIMEOUT: float = 60.0
+    HTTP_MAX_RETRIES: int = 3
+
+    # 日志配置
+    LOG_LEVEL: str = "INFO"
+    LOG_FORMAT: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+
+    # CORS配置
+    CORS_ORIGINS: list = ["*"]
+    CORS_CREDENTIALS: bool = True
+    CORS_METHODS: list = ["*"]
+    CORS_HEADERS: list = ["*"]
+
+    # LLM配置（如果需要集成）
+    LLM_PROVIDER: str = "openai"  # openai, anthropic, etc.
+    LLM_API_KEY: str = ""
+    LLM_MODEL: str = "gpt-4"
+    LLM_BASE_URL: str = ""
+
+    # 速率限制
+    RATE_LIMIT_ENABLED: bool = False
+    RATE_LIMIT_PER_MINUTE: int = 60
+
+    # 缓存配置
+    CACHE_ENABLED: bool = True
+    CACHE_TTL: int = 300  # 秒
+
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+
+# 全局设置实例
+settings = Settings()
+
+
+# 工具类别映射（用于组织和展示）
+TOOL_CATEGORIES: Dict[str, list] = {
+    "新闻搜索": [
+        "search_news",
+        "search_china_news",
+        "search_medical_news"
+    ],
+    "公司研究": [
+        "search_roadshows",
+        "search_research_reports"
+    ],
+    "概念板块": [
+        "search_concepts",
+        "get_concept_details",
+        "get_stock_concepts",
+        "get_concept_statistics"
+    ],
+    "股票分析": [
+        "search_limit_up_stocks",
+        "get_daily_stock_analysis"
+    ]
+}
+
+
+# 工具优先级（用于LLM选择工具时的提示）
+TOOL_PRIORITIES: Dict[str, int] = {
+    "search_china_news": 10,  # 最常用
+    "search_concepts": 9,
+    "search_limit_up_stocks": 8,
+    "search_research_reports": 8,
+    "get_stock_concepts": 7,
+    "search_news": 6,
+    "get_daily_stock_analysis": 5,
+    "get_concept_statistics": 5,
+    "search_medical_news": 4,
+    "search_roadshows": 4,
+    "get_concept_details": 3,
+}
+
+
+# 默认参数配置
+DEFAULT_PARAMS = {
+    "top_k": 20,
+    "page_size": 20,
+    "size": 10,
+    "sort_by": "change_pct",
+    "mode": "hybrid",
+    "exact_match": False,
+}
--- a/mcp_database.py
+++ b/mcp_database.py
@@ -0,0 +1,546 @@
+"""
+MySQL数据库查询模块
+提供股票财务数据查询功能
+"""
+
+import aiomysql
+import logging
+from typing import Dict, List, Any, Optional
+from datetime import datetime, date
+from decimal import Decimal
+import json
+
+logger = logging.getLogger(__name__)
+
+# MySQL连接配置
+MYSQL_CONFIG = {
+    'host': '222.128.1.157',
+    'port': 33060,
+    'user': 'root',
+    'password': 'Zzl5588161!',
+    'db': 'stock',
+    'charset': 'utf8mb4',
+    'autocommit': True
+}
+
+# 全局连接池
+_pool = None
+
+
+class DateTimeEncoder(json.JSONEncoder):
+    """JSON编码器，处理datetime和Decimal类型"""
+    def default(self, obj):
+        if isinstance(obj, (datetime, date)):
+            return obj.isoformat()
+        if isinstance(obj, Decimal):
+            return float(obj)
+        return super().default(obj)
+
+
+async def get_pool():
+    """获取MySQL连接池"""
+    global _pool
+    if _pool is None:
+        _pool = await aiomysql.create_pool(
+            host=MYSQL_CONFIG['host'],
+            port=MYSQL_CONFIG['port'],
+            user=MYSQL_CONFIG['user'],
+            password=MYSQL_CONFIG['password'],
+            db=MYSQL_CONFIG['db'],
+            charset=MYSQL_CONFIG['charset'],
+            autocommit=MYSQL_CONFIG['autocommit'],
+            minsize=1,
+            maxsize=10
+        )
+        logger.info("MySQL connection pool created")
+    return _pool
+
+
+async def close_pool():
+    """关闭MySQL连接池"""
+    global _pool
+    if _pool:
+        _pool.close()
+        await _pool.wait_closed()
+        _pool = None
+        logger.info("MySQL connection pool closed")
+
+
+def convert_row(row: Dict) -> Dict:
+    """转换数据库行，处理特殊类型"""
+    if not row:
+        return {}
+
+    result = {}
+    for key, value in row.items():
+        if isinstance(value, Decimal):
+            result[key] = float(value)
+        elif isinstance(value, (datetime, date)):
+            result[key] = value.isoformat()
+        else:
+            result[key] = value
+    return result
+
+
+async def get_stock_basic_info(seccode: str) -> Optional[Dict[str, Any]]:
+    """
+    获取股票基本信息
+
+    Args:
+        seccode: 股票代码
+
+    Returns:
+        股票基本信息字典
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                SELECT
+                    SECCODE, SECNAME, ORGNAME,
+                    F001V as english_name,
+                    F003V as legal_representative,
+                    F004V as registered_address,
+                    F005V as office_address,
+                    F010D as establishment_date,
+                    F011V as website,
+                    F012V as email,
+                    F013V as phone,
+                    F015V as main_business,
+                    F016V as business_scope,
+                    F017V as company_profile,
+                    F030V as industry_level1,
+                    F032V as industry_level2,
+                    F034V as sw_industry_level1,
+                    F036V as sw_industry_level2,
+                    F026V as province,
+                    F028V as city,
+                    F041V as chairman,
+                    F042V as general_manager,
+                    UPDATE_DATE as update_date
+                FROM ea_baseinfo
+                WHERE SECCODE = %s
+                LIMIT 1
+            """
+
+            await cursor.execute(query, (seccode,))
+            result = await cursor.fetchone()
+
+            if result:
+                return convert_row(result)
+            return None
+
+
+async def get_stock_financial_index(
+    seccode: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    limit: int = 10
+) -> List[Dict[str, Any]]:
+    """
+    获取股票财务指标
+
+    Args:
+        seccode: 股票代码
+        start_date: 开始日期 YYYY-MM-DD
+        end_date: 结束日期 YYYY-MM-DD
+        limit: 返回条数
+
+    Returns:
+        财务指标列表
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            # 构建查询
+            query = """
+                SELECT
+                    SECCODE, SECNAME, ENDDATE, STARTDATE,
+                    F069D as report_year,
+                    F003N as eps,  -- 每股收益
+                    F004N as basic_eps,
+                    F008N as bps,  -- 每股净资产
+                    F014N as roe,  -- 净资产收益率
+                    F016N as roa,  -- 总资产报酬率
+                    F017N as net_profit_margin,  -- 净利润率
+                    F022N as receivable_turnover,  -- 应收账款周转率
+                    F023N as inventory_turnover,  -- 存货周转率
+                    F025N as total_asset_turnover,  -- 总资产周转率
+                    F041N as debt_ratio,  -- 资产负债率
+                    F042N as current_ratio,  -- 流动比率
+                    F043N as quick_ratio,  -- 速动比率
+                    F052N as revenue_growth,  -- 营业收入增长率
+                    F053N as profit_growth,  -- 净利润增长率
+                    F089N as revenue,  -- 营业收入
+                    F090N as operating_cost,  -- 营业成本
+                    F101N as net_profit,  -- 净利润
+                    F102N as net_profit_parent  -- 归母净利润
+                FROM ea_financialindex
+                WHERE SECCODE = %s
+            """
+
+            params = [seccode]
+
+            if start_date:
+                query += " AND ENDDATE >= %s"
+                params.append(start_date)
+
+            if end_date:
+                query += " AND ENDDATE <= %s"
+                params.append(end_date)
+
+            query += " ORDER BY ENDDATE DESC LIMIT %s"
+            params.append(limit)
+
+            await cursor.execute(query, params)
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def get_stock_trade_data(
+    seccode: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    limit: int = 30
+) -> List[Dict[str, Any]]:
+    """
+    获取股票交易数据
+
+    Args:
+        seccode: 股票代码
+        start_date: 开始日期 YYYY-MM-DD
+        end_date: 结束日期 YYYY-MM-DD
+        limit: 返回条数
+
+    Returns:
+        交易数据列表
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                SELECT
+                    SECCODE, SECNAME, TRADEDATE,
+                    F002N as prev_close,  -- 昨日收盘价
+                    F003N as open_price,  -- 开盘价
+                    F005N as high_price,  -- 最高价
+                    F006N as low_price,  -- 最低价
+                    F007N as close_price,  -- 收盘价
+                    F004N as volume,  -- 成交量
+                    F011N as turnover,  -- 成交金额
+                    F009N as change_amount,  -- 涨跌额
+                    F010N as change_pct,  -- 涨跌幅
+                    F012N as turnover_rate,  -- 换手率
+                    F013N as amplitude,  -- 振幅
+                    F026N as pe_ratio,  -- 市盈率
+                    F020N as total_shares,  -- 总股本
+                    F021N as circulating_shares  -- 流通股本
+                FROM ea_trade
+                WHERE SECCODE = %s
+            """
+
+            params = [seccode]
+
+            if start_date:
+                query += " AND TRADEDATE >= %s"
+                params.append(start_date)
+
+            if end_date:
+                query += " AND TRADEDATE <= %s"
+                params.append(end_date)
+
+            query += " ORDER BY TRADEDATE DESC LIMIT %s"
+            params.append(limit)
+
+            await cursor.execute(query, params)
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def get_stock_balance_sheet(
+    seccode: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    limit: int = 8
+) -> List[Dict[str, Any]]:
+    """
+    获取资产负债表数据
+
+    Args:
+        seccode: 股票代码
+        start_date: 开始日期
+        end_date: 结束日期
+        limit: 返回条数
+
+    Returns:
+        资产负债表数据列表
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                SELECT
+                    SECCODE, SECNAME, ENDDATE,
+                    F001D as report_year,
+                    F006N as cash,  -- 货币资金
+                    F009N as receivables,  -- 应收账款
+                    F015N as inventory,  -- 存货
+                    F019N as current_assets,  -- 流动资产合计
+                    F023N as long_term_investment,  -- 长期股权投资
+                    F025N as fixed_assets,  -- 固定资产
+                    F037N as noncurrent_assets,  -- 非流动资产合计
+                    F038N as total_assets,  -- 资产总计
+                    F039N as short_term_loan,  -- 短期借款
+                    F042N as payables,  -- 应付账款
+                    F052N as current_liabilities,  -- 流动负债合计
+                    F053N as long_term_loan,  -- 长期借款
+                    F060N as noncurrent_liabilities,  -- 非流动负债合计
+                    F061N as total_liabilities,  -- 负债合计
+                    F062N as share_capital,  -- 股本
+                    F063N as capital_reserve,  -- 资本公积
+                    F065N as retained_earnings,  -- 未分配利润
+                    F070N as total_equity  -- 所有者权益合计
+                FROM ea_asset
+                WHERE SECCODE = %s
+            """
+
+            params = [seccode]
+
+            if start_date:
+                query += " AND ENDDATE >= %s"
+                params.append(start_date)
+
+            if end_date:
+                query += " AND ENDDATE <= %s"
+                params.append(end_date)
+
+            query += " ORDER BY ENDDATE DESC LIMIT %s"
+            params.append(limit)
+
+            await cursor.execute(query, params)
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def get_stock_cashflow(
+    seccode: str,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    limit: int = 8
+) -> List[Dict[str, Any]]:
+    """
+    获取现金流量表数据
+
+    Args:
+        seccode: 股票代码
+        start_date: 开始日期
+        end_date: 结束日期
+        limit: 返回条数
+
+    Returns:
+        现金流量表数据列表
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                SELECT
+                    SECCODE, SECNAME, ENDDATE, STARTDATE,
+                    F001D as report_year,
+                    F009N as operating_cash_inflow,  -- 经营活动现金流入
+                    F014N as operating_cash_outflow,  -- 经营活动现金流出
+                    F015N as net_operating_cashflow,  -- 经营活动现金流量净额
+                    F021N as investing_cash_inflow,  -- 投资活动现金流入
+                    F026N as investing_cash_outflow,  -- 投资活动现金流出
+                    F027N as net_investing_cashflow,  -- 投资活动现金流量净额
+                    F031N as financing_cash_inflow,  -- 筹资活动现金流入
+                    F035N as financing_cash_outflow,  -- 筹资活动现金流出
+                    F036N as net_financing_cashflow,  -- 筹资活动现金流量净额
+                    F039N as net_cash_increase,  -- 现金及现金等价物净增加额
+                    F044N as net_profit,  -- 净利润
+                    F046N as depreciation,  -- 固定资产折旧
+                    F060N as net_operating_cashflow_adjusted  -- 经营活动现金流量净额(补充)
+                FROM ea_cashflow
+                WHERE SECCODE = %s
+            """
+
+            params = [seccode]
+
+            if start_date:
+                query += " AND ENDDATE >= %s"
+                params.append(start_date)
+
+            if end_date:
+                query += " AND ENDDATE <= %s"
+                params.append(end_date)
+
+            query += " ORDER BY ENDDATE DESC LIMIT %s"
+            params.append(limit)
+
+            await cursor.execute(query, params)
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def search_stocks_by_criteria(
+    industry: Optional[str] = None,
+    province: Optional[str] = None,
+    min_market_cap: Optional[float] = None,
+    max_market_cap: Optional[float] = None,
+    limit: int = 50
+) -> List[Dict[str, Any]]:
+    """
+    按条件搜索股票
+
+    Args:
+        industry: 行业名称
+        province: 省份
+        min_market_cap: 最小市值（亿元）
+        max_market_cap: 最大市值（亿元）
+        limit: 返回条数
+
+    Returns:
+        股票列表
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                SELECT DISTINCT
+                    b.SECCODE,
+                    b.SECNAME,
+                    b.F030V as industry_level1,
+                    b.F032V as industry_level2,
+                    b.F034V as sw_industry_level1,
+                    b.F026V as province,
+                    b.F028V as city,
+                    b.F015V as main_business,
+                    t.F007N as latest_price,
+                    t.F010N as change_pct,
+                    t.F026N as pe_ratio,
+                    t.TRADEDATE as latest_trade_date
+                FROM ea_baseinfo b
+                LEFT JOIN (
+                    SELECT SECCODE, MAX(TRADEDATE) as max_date
+                    FROM ea_trade
+                    GROUP BY SECCODE
+                ) latest ON b.SECCODE = latest.SECCODE
+                LEFT JOIN ea_trade t ON b.SECCODE = t.SECCODE
+                    AND t.TRADEDATE = latest.max_date
+                WHERE 1=1
+            """
+
+            params = []
+
+            if industry:
+                query += " AND (b.F030V LIKE %s OR b.F032V LIKE %s OR b.F034V LIKE %s)"
+                pattern = f"%{industry}%"
+                params.extend([pattern, pattern, pattern])
+
+            if province:
+                query += " AND b.F026V = %s"
+                params.append(province)
+
+            if min_market_cap or max_market_cap:
+                # 市值 = 最新价 * 总股本 / 100000000（转换为亿元）
+                if min_market_cap:
+                    query += " AND (t.F007N * t.F020N / 100000000) >= %s"
+                    params.append(min_market_cap)
+
+                if max_market_cap:
+                    query += " AND (t.F007N * t.F020N / 100000000) <= %s"
+                    params.append(max_market_cap)
+
+            query += " ORDER BY t.TRADEDATE DESC LIMIT %s"
+            params.append(limit)
+
+            await cursor.execute(query, params)
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def get_stock_comparison(
+    seccodes: List[str],
+    metric: str = "financial"
+) -> Dict[str, Any]:
+    """
+    股票对比分析
+
+    Args:
+        seccodes: 股票代码列表
+        metric: 对比指标类型 (financial/trade)
+
+    Returns:
+        对比数据
+    """
+    pool = await get_pool()
+
+    if not seccodes or len(seccodes) < 2:
+        return {"error": "至少需要2个股票代码进行对比"}
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            placeholders = ','.join(['%s'] * len(seccodes))
+
+            if metric == "financial":
+                # 对比最新财务指标
+                query = f"""
+                    SELECT
+                        f.SECCODE, f.SECNAME, f.ENDDATE,
+                        f.F003N as eps,
+                        f.F008N as bps,
+                        f.F014N as roe,
+                        f.F017N as net_profit_margin,
+                        f.F041N as debt_ratio,
+                        f.F052N as revenue_growth,
+                        f.F053N as profit_growth,
+                        f.F089N as revenue,
+                        f.F101N as net_profit
+                    FROM ea_financialindex f
+                    INNER JOIN (
+                        SELECT SECCODE, MAX(ENDDATE) as max_date
+                        FROM ea_financialindex
+                        WHERE SECCODE IN ({placeholders})
+                        GROUP BY SECCODE
+                    ) latest ON f.SECCODE = latest.SECCODE
+                        AND f.ENDDATE = latest.max_date
+                """
+            else:  # trade
+                # 对比最新交易数据
+                query = f"""
+                    SELECT
+                        t.SECCODE, t.SECNAME, t.TRADEDATE,
+                        t.F007N as close_price,
+                        t.F010N as change_pct,
+                        t.F012N as turnover_rate,
+                        t.F026N as pe_ratio,
+                        t.F020N as total_shares,
+                        t.F021N as circulating_shares
+                    FROM ea_trade t
+                    INNER JOIN (
+                        SELECT SECCODE, MAX(TRADEDATE) as max_date
+                        FROM ea_trade
+                        WHERE SECCODE IN ({placeholders})
+                        GROUP BY SECCODE
+                    ) latest ON t.SECCODE = latest.SECCODE
+                        AND t.TRADEDATE = latest.max_date
+                """
+
+            await cursor.execute(query, seccodes)
+            results = await cursor.fetchall()
+
+            return {
+                "comparison_type": metric,
+                "stocks": [convert_row(row) for row in results]
+            }
--- a/mcp_server.py
+++ b/mcp_server.py
--- a/mcp_server_agent_integration.py
+++ b/mcp_server_agent_integration.py
@@ -0,0 +1,492 @@
+"""
+集成到 mcp_server.py 的 Agent 系统
+使用 Kimi (kimi-k2-thinking) 和 DeepMoney 两个模型
+"""
+
+from openai import OpenAI
+from pydantic import BaseModel
+from typing import List, Dict, Any, Optional, Literal
+from datetime import datetime
+import json
+import logging
+
+logger = logging.getLogger(__name__)
+
+# ==================== 模型配置 ====================
+
+# Kimi 配置 - 用于计划制定和深度推理
+KIMI_CONFIG = {
+    "api_key": "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5",
+    "base_url": "https://api.moonshot.cn/v1",
+    "model": "kimi-k2-thinking",  # 思考模型
+}
+
+# DeepMoney 配置 - 用于新闻总结
+DEEPMONEY_CONFIG = {
+    "api_key": "",  # 空值
+    "base_url": "http://111.62.35.50:8000/v1",
+    "model": "deepmoney",
+}
+
+# ==================== 数据模型 ====================
+
+class ToolCall(BaseModel):
+    """工具调用"""
+    tool: str
+    arguments: Dict[str, Any]
+    reason: str
+
+class ExecutionPlan(BaseModel):
+    """执行计划"""
+    goal: str
+    steps: List[ToolCall]
+    reasoning: str
+
+class StepResult(BaseModel):
+    """单步执行结果"""
+    step_index: int
+    tool: str
+    arguments: Dict[str, Any]
+    status: Literal["success", "failed", "skipped"]
+    result: Optional[Any] = None
+    error: Optional[str] = None
+    execution_time: float = 0
+
+class AgentResponse(BaseModel):
+    """Agent响应"""
+    success: bool
+    message: str
+    plan: Optional[ExecutionPlan] = None
+    step_results: List[StepResult] = []
+    final_summary: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+
+class ChatRequest(BaseModel):
+    """聊天请求"""
+    message: str
+    conversation_history: List[Dict[str, str]] = []
+
+# ==================== Agent 系统 ====================
+
+class MCPAgentIntegrated:
+    """集成版 MCP Agent - 使用 Kimi 和 DeepMoney"""
+
+    def __init__(self):
+        # 初始化 Kimi 客户端（计划制定）
+        self.kimi_client = OpenAI(
+            api_key=KIMI_CONFIG["api_key"],
+            base_url=KIMI_CONFIG["base_url"],
+        )
+        self.kimi_model = KIMI_CONFIG["model"]
+
+        # 初始化 DeepMoney 客户端（新闻总结）
+        self.deepmoney_client = OpenAI(
+            api_key=DEEPMONEY_CONFIG["api_key"],
+            base_url=DEEPMONEY_CONFIG["base_url"],
+        )
+        self.deepmoney_model = DEEPMONEY_CONFIG["model"]
+
+    def get_planning_prompt(self, tools: List[dict]) -> str:
+        """获取计划制定的系统提示词"""
+        tools_desc = "\n\n".join([
+            f"**{tool['name']}**\n"
+            f"描述：{tool['description']}\n"
+            f"参数：{json.dumps(tool['parameters'], ensure_ascii=False, indent=2)}"
+            for tool in tools
+        ])
+
+        return f"""你是一个专业的金融研究助手。根据用户问题，制定详细的执行计划。
+
+## 可用工具
+
+{tools_desc}
+
+## 特殊工具
+- **summarize_news**: 使用 DeepMoney 模型总结新闻数据
+  - 参数: {{"data": "新闻列表JSON", "focus": "关注点"}}
+  - 适用场景: 当需要总结新闻、研报等文本数据时
+
+## 重要知识
+- 贵州茅台: 600519
+- 涨停: 涨幅约10%
+- 概念板块: 相同题材股票分类
+
+## 任务
+分析用户问题，制定执行计划。返回 JSON：
+
+```json
+{{
+  "goal": "用户目标",
+  "reasoning": "分析思路",
+  "steps": [
+    {{
+      "tool": "工具名",
+      "arguments": {{"参数": "值"}},
+      "reason": "原因"
+    }}
+  ]
+}}
+```
+
+## 规划原则
+1. 先收集数据，再分析总结
+2. 使用 summarize_news 总结新闻类数据
+3. 不超过5个步骤
+4. 最后一步通常是总结
+
+## 示例
+
+用户："贵州茅台最近有什么新闻"
+
+计划：
+```json
+{{
+  "goal": "查询并总结贵州茅台最新新闻",
+  "reasoning": "先搜索新闻，再用 DeepMoney 总结",
+  "steps": [
+    {{
+      "tool": "search_china_news",
+      "arguments": {{"query": "贵州茅台", "top_k": 10}},
+      "reason": "搜索贵州茅台相关新闻"
+    }},
+    {{
+      "tool": "summarize_news",
+      "arguments": {{
+        "data": "前面的新闻数据",
+        "focus": "贵州茅台的重要动态和市场影响"
+      }},
+      "reason": "使用DeepMoney总结新闻要点"
+    }}
+  ]
+}}
+```
+
+只返回JSON，不要其他内容。"""
+
+    async def create_plan(self, user_query: str, tools: List[dict]) -> ExecutionPlan:
+        """阶段1: 使用 Kimi 创建执行计划（带思考过程）"""
+        logger.info(f"[Planning] Kimi开始制定计划: {user_query}")
+
+        messages = [
+            {"role": "system", "content": self.get_planning_prompt(tools)},
+            {"role": "user", "content": user_query},
+        ]
+
+        # 使用 Kimi 思考模型
+        response = self.kimi_client.chat.completions.create(
+            model=self.kimi_model,
+            messages=messages,
+            temperature=1.0,  # Kimi 推荐
+            max_tokens=16000,  # 足够容纳 reasoning_content
+        )
+
+        choice = response.choices[0]
+        message = choice.message
+
+        # 提取思考过程
+        reasoning_content = ""
+        if hasattr(message, "reasoning_content"):
+            reasoning_content = getattr(message, "reasoning_content")
+            logger.info(f"[Planning] Kimi思考过程: {reasoning_content[:200]}...")
+
+        # 提取计划内容
+        plan_json = message.content.strip()
+
+        # 清理可能的代码块标记
+        if "```json" in plan_json:
+            plan_json = plan_json.split("```json")[1].split("```")[0].strip()
+        elif "```" in plan_json:
+            plan_json = plan_json.split("```")[1].split("```")[0].strip()
+
+        plan_data = json.loads(plan_json)
+
+        plan = ExecutionPlan(
+            goal=plan_data["goal"],
+            reasoning=plan_data.get("reasoning", "") + "\n\n" + (reasoning_content[:500] if reasoning_content else ""),
+            steps=[ToolCall(**step) for step in plan_data["steps"]],
+        )
+
+        logger.info(f"[Planning] 计划制定完成: {len(plan.steps)} 步")
+        return plan
+
+    async def execute_tool(
+        self,
+        tool_name: str,
+        arguments: Dict[str, Any],
+        tool_handlers: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """执行单个工具"""
+
+        # 特殊工具：summarize_news（使用 DeepMoney）
+        if tool_name == "summarize_news":
+            return await self.summarize_news_with_deepmoney(
+                data=arguments.get("data", ""),
+                focus=arguments.get("focus", "关键信息"),
+            )
+
+        # 调用 MCP 工具
+        handler = tool_handlers.get(tool_name)
+        if not handler:
+            raise ValueError(f"Tool '{tool_name}' not found")
+
+        result = await handler(arguments)
+        return result
+
+    async def summarize_news_with_deepmoney(self, data: str, focus: str) -> str:
+        """使用 DeepMoney 模型总结新闻"""
+        logger.info(f"[DeepMoney] 总结新闻，关注点: {focus}")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "你是一个专业的金融新闻分析师，擅长提取关键信息并进行总结。"
+            },
+            {
+                "role": "user",
+                "content": f"请总结以下新闻数据，关注点：{focus}\n\n数据：\n{data[:3000]}"
+            },
+        ]
+
+        try:
+            response = self.deepmoney_client.chat.completions.create(
+                model=self.deepmoney_model,
+                messages=messages,
+                temperature=0.7,
+                max_tokens=1000,
+            )
+
+            summary = response.choices[0].message.content
+            logger.info(f"[DeepMoney] 总结完成")
+            return summary
+
+        except Exception as e:
+            logger.error(f"[DeepMoney] 总结失败: {str(e)}")
+            # 降级：返回简化摘要
+            return f"新闻总结失败，原始数据：{data[:500]}..."
+
+    async def execute_plan(
+        self,
+        plan: ExecutionPlan,
+        tool_handlers: Dict[str, Any],
+    ) -> List[StepResult]:
+        """阶段2: 执行计划"""
+        logger.info(f"[Execution] 开始执行: {len(plan.steps)} 步")
+
+        results = []
+        collected_data = {}
+
+        for i, step in enumerate(plan.steps):
+            logger.info(f"[Execution] 步骤 {i+1}/{len(plan.steps)}: {step.tool}")
+
+            start_time = datetime.now()
+
+            try:
+                # 替换占位符
+                arguments = step.arguments.copy()
+
+                # 如果参数值是 "前面的新闻数据" 或 "前面收集的所有数据"
+                if step.tool == "summarize_news":
+                    if arguments.get("data") in ["前面的新闻数据", "前面收集的所有数据"]:
+                        # 将收集的数据传递
+                        arguments["data"] = json.dumps(collected_data, ensure_ascii=False, indent=2)
+
+                # 执行工具
+                result = await self.execute_tool(step.tool, arguments, tool_handlers)
+
+                execution_time = (datetime.now() - start_time).total_seconds()
+
+                step_result = StepResult(
+                    step_index=i,
+                    tool=step.tool,
+                    arguments=arguments,
+                    status="success",
+                    result=result,
+                    execution_time=execution_time,
+                )
+                results.append(step_result)
+
+                # 收集数据
+                collected_data[f"step_{i+1}_{step.tool}"] = result
+
+                logger.info(f"[Execution] 步骤 {i+1} 完成: {execution_time:.2f}s")
+
+            except Exception as e:
+                logger.error(f"[Execution] 步骤 {i+1} 失败: {str(e)}")
+
+                execution_time = (datetime.now() - start_time).total_seconds()
+
+                step_result = StepResult(
+                    step_index=i,
+                    tool=step.tool,
+                    arguments=step.arguments,
+                    status="failed",
+                    error=str(e),
+                    execution_time=execution_time,
+                )
+                results.append(step_result)
+
+                # 继续执行其他步骤
+                continue
+
+        logger.info(f"[Execution] 执行完成")
+        return results
+
+    async def generate_final_summary(
+        self,
+        user_query: str,
+        plan: ExecutionPlan,
+        step_results: List[StepResult],
+    ) -> str:
+        """阶段3: 使用 Kimi 生成最终总结"""
+        logger.info("[Summary] Kimi生成最终总结")
+
+        # 收集成功的结果
+        successful_results = [r for r in step_results if r.status == "success"]
+
+        if not successful_results:
+            return "很抱歉，所有步骤都执行失败，无法生成分析报告。"
+
+        # 构建结果文本（精简版）
+        results_text = "\n\n".join([
+            f"**步骤 {r.step_index + 1}: {r.tool}**\n"
+            f"结果: {str(r.result)[:800]}..."
+            for r in successful_results[:3]  # 只取前3个，避免超长
+        ])
+
+        messages = [
+            {
+                "role": "system",
+                "content": "你是专业的金融研究助手。根据执行结果，生成简洁清晰的报告。"
+            },
+            {
+                "role": "user",
+                "content": f"""用户问题：{user_query}
+
+执行计划：{plan.goal}
+
+执行结果：
+{results_text}
+
+请生成专业的分析报告（300字以内）。"""
+            },
+        ]
+
+        try:
+            response = self.kimi_client.chat.completions.create(
+                model="kimi-k2-turbpreview",  # 使用非思考模型，更快
+                messages=messages,
+                temperature=0.7,
+                max_tokens=1000,
+            )
+
+            summary = response.choices[0].message.content
+            logger.info("[Summary] 总结完成")
+            return summary
+
+        except Exception as e:
+            logger.error(f"[Summary] 总结失败: {str(e)}")
+            # 降级：返回最后一步的结果
+            if successful_results:
+                last_result = successful_results[-1]
+                if isinstance(last_result.result, str):
+                    return last_result.result
+                else:
+                    return json.dumps(last_result.result, ensure_ascii=False, indent=2)
+            return "总结生成失败"
+
+    async def process_query(
+        self,
+        user_query: str,
+        tools: List[dict],
+        tool_handlers: Dict[str, Any],
+    ) -> AgentResponse:
+        """主流程"""
+        logger.info(f"[Agent] 处理查询: {user_query}")
+
+        try:
+            # 阶段1: Kimi 制定计划
+            plan = await self.create_plan(user_query, tools)
+
+            # 阶段2: 执行工具
+            step_results = await self.execute_plan(plan, tool_handlers)
+
+            # 阶段3: Kimi 生成总结
+            final_summary = await self.generate_final_summary(
+                user_query, plan, step_results
+            )
+
+            return AgentResponse(
+                success=True,
+                message=final_summary,
+                plan=plan,
+                step_results=step_results,
+                final_summary=final_summary,
+                metadata={
+                    "total_steps": len(plan.steps),
+                    "successful_steps": len([r for r in step_results if r.status == "success"]),
+                    "failed_steps": len([r for r in step_results if r.status == "failed"]),
+                    "total_execution_time": sum(r.execution_time for r in step_results),
+                    "model_used": {
+                        "planning": self.kimi_model,
+                        "summarization": "kimi-k2-turbpreview",
+                        "news_summary": self.deepmoney_model,
+                    },
+                },
+            )
+
+        except Exception as e:
+            logger.error(f"[Agent] 错误: {str(e)}", exc_info=True)
+            return AgentResponse(
+                success=False,
+                message=f"处理失败: {str(e)}",
+            )
+
+# ==================== 添加到 mcp_server.py ====================
+
+"""
+在 mcp_server.py 中添加以下代码：
+
+# 导入 Agent 系统
+from mcp_server_agent_integration import MCPAgentIntegrated, ChatRequest, AgentResponse
+
+# 创建 Agent 实例（全局）
+agent = MCPAgentIntegrated()
+
+# 添加端点
+@app.post("/agent/chat", response_model=AgentResponse)
+async def agent_chat(request: ChatRequest):
+    \"\"\"智能代理对话端点\"\"\"
+    logger.info(f"Agent chat: {request.message}")
+
+    # 获取工具列表
+    tools = [tool.dict() for tool in TOOLS]
+
+    # 添加特殊工具：summarize_news
+    tools.append({
+        "name": "summarize_news",
+        "description": "使用 DeepMoney 模型总结新闻数据，提取关键信息",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "data": {
+                    "type": "string",
+                    "description": "要总结的新闻数据（JSON格式）"
+                },
+                "focus": {
+                    "type": "string",
+                    "description": "关注点，例如：'市场影响'、'投资机会'等"
+                }
+            },
+            "required": ["data"]
+        }
+    })
+
+    # 处理查询
+    response = await agent.process_query(
+        user_query=request.message,
+        tools=tools,
+        tool_handlers=TOOL_HANDLERS,
+    )
+
+    return response
+"""
--- a/src/components/ChatBot/ChatInterface.js
+++ b/src/components/ChatBot/ChatInterface.js
@@ -0,0 +1,376 @@
+// src/components/ChatBot/ChatInterface.js
+// 聊天界面主组件
+
+import React, { useState, useRef, useEffect } from 'react';
+import {
+  Box,
+  Flex,
+  Input,
+  IconButton,
+  VStack,
+  HStack,
+  Text,
+  Spinner,
+  useColorModeValue,
+  useToast,
+  Divider,
+  Badge,
+  Menu,
+  MenuButton,
+  MenuList,
+  MenuItem,
+  Button,
+} from '@chakra-ui/react';
+import { FiSend, FiRefreshCw, FiSettings, FiDownload } from 'react-icons/fi';
+import { ChevronDownIcon } from '@chakra-ui/icons';
+import MessageBubble from './MessageBubble';
+import { mcpService } from '../../services/mcpService';
+import { logger } from '../../utils/logger';
+
+/**
+ * 聊天界面组件
+ */
+export const ChatInterface = () => {
+  const [messages, setMessages] = useState([
+    {
+      id: 1,
+      content: '你好！我是AI投资助手，我可以帮你查询股票信息、新闻资讯、概念板块、涨停分析等。请问有什么可以帮到你的？',
+      isUser: false,
+      type: 'text',
+      timestamp: new Date().toISOString(),
+    },
+  ]);
+  const [inputValue, setInputValue] = useState('');
+  const [isLoading, setIsLoading] = useState(false);
+  const [availableTools, setAvailableTools] = useState([]);
+
+  const messagesEndRef = useRef(null);
+  const inputRef = useRef(null);
+  const toast = useToast();
+
+  // 颜色主题
+  const bgColor = useColorModeValue('white', 'gray.800');
+  const borderColor = useColorModeValue('gray.200', 'gray.600');
+  const inputBg = useColorModeValue('gray.50', 'gray.700');
+
+  // 加载可用工具列表
+  useEffect(() => {
+    const loadTools = async () => {
+      const result = await mcpService.listTools();
+      if (result.success) {
+        setAvailableTools(result.data);
+        logger.info('ChatInterface', '已加载MCP工具', { count: result.data.length });
+      }
+    };
+    loadTools();
+  }, []);
+
+  // 自动滚动到底部
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  };
+
+  useEffect(() => {
+    scrollToBottom();
+  }, [messages]);
+
+  // 发送消息
+  const handleSendMessage = async () => {
+    if (!inputValue.trim() || isLoading) return;
+
+    const userMessage = {
+      id: Date.now(),
+      content: inputValue,
+      isUser: true,
+      type: 'text',
+      timestamp: new Date().toISOString(),
+    };
+
+    setMessages((prev) => [...prev, userMessage]);
+    setInputValue('');
+    setIsLoading(true);
+
+    try {
+      // 调用MCP服务
+      const response = await mcpService.chat(inputValue, messages);
+
+      let botMessage;
+      if (response.success) {
+        // 根据返回的数据类型构造消息
+        const data = response.data;
+
+        if (typeof data === 'string') {
+          botMessage = {
+            id: Date.now() + 1,
+            content: data,
+            isUser: false,
+            type: 'text',
+            timestamp: new Date().toISOString(),
+          };
+        } else if (Array.isArray(data)) {
+          // 数据列表
+          botMessage = {
+            id: Date.now() + 1,
+            content: `找到 ${data.length} 条结果：`,
+            isUser: false,
+            type: 'data',
+            data: data,
+            timestamp: new Date().toISOString(),
+          };
+        } else if (typeof data === 'object') {
+          // 对象数据
+          botMessage = {
+            id: Date.now() + 1,
+            content: JSON.stringify(data, null, 2),
+            isUser: false,
+            type: 'markdown',
+            timestamp: new Date().toISOString(),
+          };
+        } else {
+          botMessage = {
+            id: Date.now() + 1,
+            content: '抱歉，我无法理解这个查询结果。',
+            isUser: false,
+            type: 'text',
+            timestamp: new Date().toISOString(),
+          };
+        }
+      } else {
+        botMessage = {
+          id: Date.now() + 1,
+          content: `抱歉，查询失败：${response.error}`,
+          isUser: false,
+          type: 'text',
+          timestamp: new Date().toISOString(),
+        };
+      }
+
+      setMessages((prev) => [...prev, botMessage]);
+    } catch (error) {
+      logger.error('ChatInterface', 'handleSendMessage', error);
+      const errorMessage = {
+        id: Date.now() + 1,
+        content: `抱歉，发生了错误：${error.message}`,
+        isUser: false,
+        type: 'text',
+        timestamp: new Date().toISOString(),
+      };
+      setMessages((prev) => [...prev, errorMessage]);
+    } finally {
+      setIsLoading(false);
+      inputRef.current?.focus();
+    }
+  };
+
+  // 处理键盘事件
+  const handleKeyPress = (e) => {
+    if (e.key === 'Enter' && !e.shiftKey) {
+      e.preventDefault();
+      handleSendMessage();
+    }
+  };
+
+  // 清空对话
+  const handleClearChat = () => {
+    setMessages([
+      {
+        id: 1,
+        content: '对话已清空。有什么可以帮到你的？',
+        isUser: false,
+        type: 'text',
+        timestamp: new Date().toISOString(),
+      },
+    ]);
+  };
+
+  // 复制消息
+  const handleCopyMessage = () => {
+    toast({
+      title: '已复制',
+      status: 'success',
+      duration: 2000,
+      isClosable: true,
+    });
+  };
+
+  // 反馈
+  const handleFeedback = (type) => {
+    logger.info('ChatInterface', 'Feedback', { type });
+    toast({
+      title: type === 'positive' ? '感谢反馈！' : '我们会改进',
+      status: 'info',
+      duration: 2000,
+      isClosable: true,
+    });
+  };
+
+  // 快捷问题
+  const quickQuestions = [
+    '查询贵州茅台的股票信息',
+    '搜索人工智能相关新闻',
+    '今日涨停股票有哪些',
+    '新能源概念板块分析',
+  ];
+
+  const handleQuickQuestion = (question) => {
+    setInputValue(question);
+    inputRef.current?.focus();
+  };
+
+  // 导出对话
+  const handleExportChat = () => {
+    const chatText = messages
+      .map((msg) => `[${msg.isUser ? '用户' : 'AI'}] ${msg.content}`)
+      .join('\n\n');
+
+    const blob = new Blob([chatText], { type: 'text/plain' });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = `chat_${new Date().toISOString().slice(0, 10)}.txt`;
+    a.click();
+    URL.revokeObjectURL(url);
+  };
+
+  return (
+    <Flex direction="column" h="100%" bg={bgColor}>
+      {/* 头部工具栏 */}
+      <Flex
+        px={4}
+        py={3}
+        borderBottom="1px"
+        borderColor={borderColor}
+        align="center"
+        justify="space-between"
+      >
+        <HStack>
+          <Text fontWeight="bold" fontSize="lg">AI投资助手</Text>
+          <Badge colorScheme="green">在线</Badge>
+          {availableTools.length > 0 && (
+            <Badge colorScheme="blue">{availableTools.length} 个工具</Badge>
+          )}
+        </HStack>
+        <HStack>
+          <IconButton
+            icon={<FiRefreshCw />}
+            size="sm"
+            variant="ghost"
+            aria-label="清空对话"
+            onClick={handleClearChat}
+          />
+          <IconButton
+            icon={<FiDownload />}
+            size="sm"
+            variant="ghost"
+            aria-label="导出对话"
+            onClick={handleExportChat}
+          />
+          <Menu>
+            <MenuButton
+              as={IconButton}
+              icon={<FiSettings />}
+              size="sm"
+              variant="ghost"
+              aria-label="设置"
+            />
+            <MenuList>
+              <MenuItem>模型设置</MenuItem>
+              <MenuItem>快捷指令</MenuItem>
+              <MenuItem>历史记录</MenuItem>
+            </MenuList>
+          </Menu>
+        </HStack>
+      </Flex>
+
+      {/* 消息列表 */}
+      <Box
+        flex="1"
+        overflowY="auto"
+        px={4}
+        py={4}
+        css={{
+          '&::-webkit-scrollbar': {
+            width: '8px',
+          },
+          '&::-webkit-scrollbar-track': {
+            background: 'transparent',
+          },
+          '&::-webkit-scrollbar-thumb': {
+            background: '#CBD5E0',
+            borderRadius: '4px',
+          },
+        }}
+      >
+        <VStack spacing={0} align="stretch">
+          {messages.map((message) => (
+            <MessageBubble
+              key={message.id}
+              message={message}
+              isUser={message.isUser}
+              onCopy={handleCopyMessage}
+              onFeedback={handleFeedback}
+            />
+          ))}
+          {isLoading && (
+            <Flex justify="flex-start" mb={4}>
+              <Flex align="center" bg={inputBg} px={4} py={3} borderRadius="lg">
+                <Spinner size="sm" mr={2} />
+                <Text fontSize="sm">AI正在思考...</Text>
+              </Flex>
+            </Flex>
+          )}
+          <div ref={messagesEndRef} />
+        </VStack>
+      </Box>
+
+      {/* 快捷问题（仅在消息较少时显示） */}
+      {messages.length <= 2 && (
+        <Box px={4} py={2}>
+          <Text fontSize="xs" color="gray.500" mb={2}>快捷问题：</Text>
+          <Flex wrap="wrap" gap={2}>
+            {quickQuestions.map((question, idx) => (
+              <Button
+                key={idx}
+                size="xs"
+                variant="outline"
+                onClick={() => handleQuickQuestion(question)}
+              >
+                {question}
+              </Button>
+            ))}
+          </Flex>
+        </Box>
+      )}
+
+      <Divider />
+
+      {/* 输入框 */}
+      <Box px={4} py={3} borderTop="1px" borderColor={borderColor}>
+        <Flex>
+          <Input
+            ref={inputRef}
+            value={inputValue}
+            onChange={(e) => setInputValue(e.target.value)}
+            onKeyPress={handleKeyPress}
+            placeholder="输入消息... (Shift+Enter换行，Enter发送)"
+            bg={inputBg}
+            border="none"
+            _focus={{ boxShadow: 'none' }}
+            mr={2}
+            disabled={isLoading}
+          />
+          <IconButton
+            icon={<FiSend />}
+            colorScheme="blue"
+            aria-label="发送"
+            onClick={handleSendMessage}
+            isLoading={isLoading}
+            disabled={!inputValue.trim()}
+          />
+        </Flex>
+      </Box>
+    </Flex>
+  );
+};
+
+export default ChatInterface;
--- a/src/components/ChatBot/ChatInterfaceV2.js
+++ b/src/components/ChatBot/ChatInterfaceV2.js
@@ -0,0 +1,576 @@
+// src/components/ChatBot/ChatInterfaceV2.js
+// 重新设计的聊天界面 - 更漂亮、支持Agent模式
+
+import React, { useState, useRef, useEffect } from 'react';
+import {
+  Box,
+  Flex,
+  Input,
+  IconButton,
+  VStack,
+  HStack,
+  Text,
+  Spinner,
+  useColorModeValue,
+  useToast,
+  Divider,
+  Badge,
+  Button,
+  Avatar,
+  Heading,
+  Progress,
+  Fade,
+} from '@chakra-ui/react';
+import { FiSend, FiRefreshCw, FiDownload, FiCpu, FiUser, FiZap } from 'react-icons/fi';
+import { PlanCard } from './PlanCard';
+import { StepResultCard } from './StepResultCard';
+import { mcpService } from '../../services/mcpService';
+import { logger } from '../../utils/logger';
+
+/**
+ * Agent消息类型
+ */
+const MessageTypes = {
+  USER: 'user',
+  AGENT_THINKING: 'agent_thinking',
+  AGENT_PLAN: 'agent_plan',
+  AGENT_EXECUTING: 'agent_executing',
+  AGENT_RESPONSE: 'agent_response',
+  ERROR: 'error',
+};
+
+/**
+ * 聊天界面V2组件 - Agent模式
+ */
+export const ChatInterfaceV2 = () => {
+  const [messages, setMessages] = useState([
+    {
+      id: 1,
+      type: MessageTypes.AGENT_RESPONSE,
+      content: '你好！我是AI投资研究助手。我会通过多步骤分析来帮助你深入了解金融市场。\n\n你可以问我：\n• 全面分析某只股票\n• 某个行业的投资机会\n• 今日市场热点\n• 某个概念板块的表现',
+      timestamp: new Date().toISOString(),
+    },
+  ]);
+  const [inputValue, setInputValue] = useState('');
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [currentProgress, setCurrentProgress] = useState(0);
+
+  const messagesEndRef = useRef(null);
+  const inputRef = useRef(null);
+  const toast = useToast();
+
+  // 颜色主题
+  const bgColor = useColorModeValue('gray.50', 'gray.900');
+  const chatBg = useColorModeValue('white', 'gray.800');
+  const inputBg = useColorModeValue('white', 'gray.700');
+  const userBubbleBg = useColorModeValue('blue.500', 'blue.600');
+  const agentBubbleBg = useColorModeValue('white', 'gray.700');
+  const borderColor = useColorModeValue('gray.200', 'gray.600');
+
+  // 自动滚动到底部
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  };
+
+  useEffect(() => {
+    scrollToBottom();
+  }, [messages]);
+
+  // 添加消息
+  const addMessage = (message) => {
+    setMessages((prev) => [...prev, { ...message, id: Date.now() }]);
+  };
+
+  // 更新最后一条消息
+  const updateLastMessage = (updates) => {
+    setMessages((prev) => {
+      const newMessages = [...prev];
+      if (newMessages.length > 0) {
+        newMessages[newMessages.length - 1] = {
+          ...newMessages[newMessages.length - 1],
+          ...updates,
+        };
+      }
+      return newMessages;
+    });
+  };
+
+  // 发送消息（Agent模式）
+  const handleSendMessage = async () => {
+    if (!inputValue.trim() || isProcessing) return;
+
+    const userMessage = {
+      type: MessageTypes.USER,
+      content: inputValue,
+      timestamp: new Date().toISOString(),
+    };
+
+    addMessage(userMessage);
+    setInputValue('');
+    setIsProcessing(true);
+    setCurrentProgress(0);
+
+    try {
+      // 1. 显示思考状态
+      addMessage({
+        type: MessageTypes.AGENT_THINKING,
+        content: '正在分析你的问题...',
+        timestamp: new Date().toISOString(),
+      });
+
+      setCurrentProgress(10);
+
+      // 调用 Agent API
+      const response = await fetch(`${mcpService.baseURL.replace('/mcp', '')}/mcp/agent/chat`, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({
+          message: inputValue,
+          conversation_history: messages.filter(m => m.type === MessageTypes.USER || m.type === MessageTypes.AGENT_RESPONSE).map(m => ({
+            isUser: m.type === MessageTypes.USER,
+            content: m.content,
+          })),
+        }),
+      });
+
+      if (!response.ok) {
+        throw new Error('Agent请求失败');
+      }
+
+      const agentResponse = await response.json();
+      logger.info('Agent response', agentResponse);
+
+      // 移除思考消息
+      setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_THINKING));
+
+      if (!agentResponse.success) {
+        throw new Error(agentResponse.message || '处理失败');
+      }
+
+      setCurrentProgress(30);
+
+      // 2. 显示执行计划
+      if (agentResponse.plan) {
+        addMessage({
+          type: MessageTypes.AGENT_PLAN,
+          content: '已制定执行计划',
+          plan: agentResponse.plan,
+          timestamp: new Date().toISOString(),
+        });
+      }
+
+      setCurrentProgress(40);
+
+      // 3. 显示执行过程
+      if (agentResponse.step_results && agentResponse.step_results.length > 0) {
+        addMessage({
+          type: MessageTypes.AGENT_EXECUTING,
+          content: '正在执行步骤...',
+          plan: agentResponse.plan,
+          stepResults: agentResponse.step_results,
+          timestamp: new Date().toISOString(),
+        });
+
+        // 模拟进度更新
+        for (let i = 0; i < agentResponse.step_results.length; i++) {
+          setCurrentProgress(40 + (i + 1) / agentResponse.step_results.length * 50);
+          await new Promise(resolve => setTimeout(resolve, 100));
+        }
+      }
+
+      setCurrentProgress(100);
+
+      // 移除执行中消息
+      setMessages(prev => prev.filter(m => m.type !== MessageTypes.AGENT_EXECUTING));
+
+      // 4. 显示最终结果
+      addMessage({
+        type: MessageTypes.AGENT_RESPONSE,
+        content: agentResponse.message || agentResponse.final_summary,
+        plan: agentResponse.plan,
+        stepResults: agentResponse.step_results,
+        metadata: agentResponse.metadata,
+        timestamp: new Date().toISOString(),
+      });
+
+    } catch (error) {
+      logger.error('Agent chat error', error);
+
+      // 移除思考/执行中消息
+      setMessages(prev => prev.filter(
+        m => m.type !== MessageTypes.AGENT_THINKING && m.type !== MessageTypes.AGENT_EXECUTING
+      ));
+
+      addMessage({
+        type: MessageTypes.ERROR,
+        content: `处理失败：${error.message}`,
+        timestamp: new Date().toISOString(),
+      });
+
+      toast({
+        title: '处理失败',
+        description: error.message,
+        status: 'error',
+        duration: 3000,
+        isClosable: true,
+      });
+    } finally {
+      setIsProcessing(false);
+      setCurrentProgress(0);
+      inputRef.current?.focus();
+    }
+  };
+
+  // 处理键盘事件
+  const handleKeyPress = (e) => {
+    if (e.key === 'Enter' && !e.shiftKey) {
+      e.preventDefault();
+      handleSendMessage();
+    }
+  };
+
+  // 清空对话
+  const handleClearChat = () => {
+    setMessages([
+      {
+        id: 1,
+        type: MessageTypes.AGENT_RESPONSE,
+        content: '对话已清空。有什么可以帮到你的？',
+        timestamp: new Date().toISOString(),
+      },
+    ]);
+  };
+
+  // 导出对话
+  const handleExportChat = () => {
+    const chatText = messages
+      .filter(m => m.type === MessageTypes.USER || m.type === MessageTypes.AGENT_RESPONSE)
+      .map((msg) => `[${msg.type === MessageTypes.USER ? '用户' : 'AI助手'}] ${msg.content}`)
+      .join('\n\n');
+
+    const blob = new Blob([chatText], { type: 'text/plain' });
+    const url = URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = `chat_${new Date().toISOString().slice(0, 10)}.txt`;
+    a.click();
+    URL.revokeObjectURL(url);
+  };
+
+  // 快捷问题
+  const quickQuestions = [
+    '全面分析贵州茅台这只股票',
+    '今日涨停股票有哪些亮点',
+    '新能源概念板块的投资机会',
+    '半导体行业最新动态',
+  ];
+
+  return (
+    <Flex direction="column" h="100%" bg={bgColor}>
+      {/* 头部 */}
+      <Box
+        bg={chatBg}
+        borderBottom="1px"
+        borderColor={borderColor}
+        px={6}
+        py={4}
+      >
+        <HStack justify="space-between">
+          <HStack spacing={4}>
+            <Avatar
+              size="md"
+              bg="blue.500"
+              icon={<FiCpu fontSize="1.5rem" />}
+            />
+            <VStack align="start" spacing={0}>
+              <Heading size="md">AI投资研究助手</Heading>
+              <HStack>
+                <Badge colorScheme="green" fontSize="xs">
+                  <HStack spacing={1}>
+                    <FiZap size={10} />
+                    <span>智能分析</span>
+                  </HStack>
+                </Badge>
+                <Text fontSize="xs" color="gray.500">
+                  多步骤深度研究
+                </Text>
+              </HStack>
+            </VStack>
+          </HStack>
+
+          <HStack>
+            <IconButton
+              icon={<FiRefreshCw />}
+              size="sm"
+              variant="ghost"
+              aria-label="清空对话"
+              onClick={handleClearChat}
+            />
+            <IconButton
+              icon={<FiDownload />}
+              size="sm"
+              variant="ghost"
+              aria-label="导出对话"
+              onClick={handleExportChat}
+            />
+          </HStack>
+        </HStack>
+
+        {/* 进度条 */}
+        {isProcessing && (
+          <Progress
+            value={currentProgress}
+            size="xs"
+            colorScheme="blue"
+            mt={3}
+            borderRadius="full"
+            isAnimated
+          />
+        )}
+      </Box>
+
+      {/* 消息列表 */}
+      <Box
+        flex="1"
+        overflowY="auto"
+        px={6}
+        py={4}
+        css={{
+          '&::-webkit-scrollbar': {
+            width: '8px',
+          },
+          '&::-webkit-scrollbar-track': {
+            background: 'transparent',
+          },
+          '&::-webkit-scrollbar-thumb': {
+            background: '#CBD5E0',
+            borderRadius: '4px',
+          },
+        }}
+      >
+        <VStack spacing={4} align="stretch">
+          {messages.map((message) => (
+            <Fade in key={message.id}>
+              <MessageRenderer message={message} />
+            </Fade>
+          ))}
+          <div ref={messagesEndRef} />
+        </VStack>
+      </Box>
+
+      {/* 快捷问题 */}
+      {messages.length <= 2 && !isProcessing && (
+        <Box px={6} py={3} bg={chatBg} borderTop="1px" borderColor={borderColor}>
+          <Text fontSize="xs" color="gray.500" mb={2}>💡 试试这些问题：</Text>
+          <Flex wrap="wrap" gap={2}>
+            {quickQuestions.map((question, idx) => (
+              <Button
+                key={idx}
+                size="sm"
+                variant="outline"
+                colorScheme="blue"
+                fontSize="xs"
+                onClick={() => {
+                  setInputValue(question);
+                  inputRef.current?.focus();
+                }}
+              >
+                {question}
+              </Button>
+            ))}
+          </Flex>
+        </Box>
+      )}
+
+      {/* 输入框 */}
+      <Box px={6} py={4} bg={chatBg} borderTop="1px" borderColor={borderColor}>
+        <Flex>
+          <Input
+            ref={inputRef}
+            value={inputValue}
+            onChange={(e) => setInputValue(e.target.value)}
+            onKeyPress={handleKeyPress}
+            placeholder="输入你的问题，我会进行深度分析..."
+            bg={inputBg}
+            border="1px"
+            borderColor={borderColor}
+            _focus={{ borderColor: 'blue.500', boxShadow: '0 0 0 1px #3182CE' }}
+            mr={2}
+            disabled={isProcessing}
+            size="lg"
+          />
+          <IconButton
+            icon={isProcessing ? <Spinner size="sm" /> : <FiSend />}
+            colorScheme="blue"
+            aria-label="发送"
+            onClick={handleSendMessage}
+            isLoading={isProcessing}
+            disabled={!inputValue.trim() || isProcessing}
+            size="lg"
+          />
+        </Flex>
+      </Box>
+    </Flex>
+  );
+};
+
+/**
+ * 消息渲染器
+ */
+const MessageRenderer = ({ message }) => {
+  const userBubbleBg = useColorModeValue('blue.500', 'blue.600');
+  const agentBubbleBg = useColorModeValue('white', 'gray.700');
+  const borderColor = useColorModeValue('gray.200', 'gray.600');
+
+  switch (message.type) {
+    case MessageTypes.USER:
+      return (
+        <Flex justify="flex-end">
+          <HStack align="flex-start" maxW="75%">
+            <Box
+              bg={userBubbleBg}
+              color="white"
+              px={4}
+              py={3}
+              borderRadius="lg"
+              boxShadow="md"
+            >
+              <Text fontSize="sm" whiteSpace="pre-wrap">
+                {message.content}
+              </Text>
+            </Box>
+            <Avatar size="sm" bg="blue.500" icon={<FiUser fontSize="1rem" />} />
+          </HStack>
+        </Flex>
+      );
+
+    case MessageTypes.AGENT_THINKING:
+      return (
+        <Flex justify="flex-start">
+          <HStack align="flex-start" maxW="75%">
+            <Avatar size="sm" bg="purple.500" icon={<FiCpu fontSize="1rem" />} />
+            <Box
+              bg={agentBubbleBg}
+              px={4}
+              py={3}
+              borderRadius="lg"
+              borderWidth="1px"
+              borderColor={borderColor}
+              boxShadow="sm"
+            >
+              <HStack>
+                <Spinner size="sm" color="purple.500" />
+                <Text fontSize="sm" color="purple.600">
+                  {message.content}
+                </Text>
+              </HStack>
+            </Box>
+          </HStack>
+        </Flex>
+      );
+
+    case MessageTypes.AGENT_PLAN:
+      return (
+        <Flex justify="flex-start">
+          <HStack align="flex-start" maxW="85%">
+            <Avatar size="sm" bg="blue.500" icon={<FiCpu fontSize="1rem" />} />
+            <VStack align="stretch" flex={1}>
+              <PlanCard plan={message.plan} stepResults={[]} />
+            </VStack>
+          </HStack>
+        </Flex>
+      );
+
+    case MessageTypes.AGENT_EXECUTING:
+      return (
+        <Flex justify="flex-start">
+          <HStack align="flex-start" maxW="85%">
+            <Avatar size="sm" bg="orange.500" icon={<FiCpu fontSize="1rem" />} />
+            <VStack align="stretch" flex={1} spacing={3}>
+              <PlanCard plan={message.plan} stepResults={message.stepResults} />
+              {message.stepResults?.map((result, idx) => (
+                <StepResultCard key={idx} stepResult={result} />
+              ))}
+            </VStack>
+          </HStack>
+        </Flex>
+      );
+
+    case MessageTypes.AGENT_RESPONSE:
+      return (
+        <Flex justify="flex-start">
+          <HStack align="flex-start" maxW="85%">
+            <Avatar size="sm" bg="green.500" icon={<FiCpu fontSize="1rem" />} />
+            <VStack align="stretch" flex={1} spacing={3}>
+              {/* 最终总结 */}
+              <Box
+                bg={agentBubbleBg}
+                px={4}
+                py={3}
+                borderRadius="lg"
+                borderWidth="1px"
+                borderColor={borderColor}
+                boxShadow="md"
+              >
+                <Text fontSize="sm" whiteSpace="pre-wrap">
+                  {message.content}
+                </Text>
+
+                {/* 元数据 */}
+                {message.metadata && (
+                  <HStack mt={3} spacing={4} fontSize="xs" color="gray.500">
+                    <Text>总步骤: {message.metadata.total_steps}</Text>
+                    <Text>✓ {message.metadata.successful_steps}</Text>
+                    {message.metadata.failed_steps > 0 && (
+                      <Text>✗ {message.metadata.failed_steps}</Text>
+                    )}
+                    <Text>耗时: {message.metadata.total_execution_time?.toFixed(1)}s</Text>
+                  </HStack>
+                )}
+              </Box>
+
+              {/* 执行详情（可选） */}
+              {message.plan && message.stepResults && message.stepResults.length > 0 && (
+                <VStack align="stretch" spacing={2}>
+                  <Divider />
+                  <Text fontSize="xs" fontWeight="bold" color="gray.500">
+                    📊 执行详情（点击展开查看）
+                  </Text>
+                  {message.stepResults.map((result, idx) => (
+                    <StepResultCard key={idx} stepResult={result} />
+                  ))}
+                </VStack>
+              )}
+            </VStack>
+          </HStack>
+        </Flex>
+      );
+
+    case MessageTypes.ERROR:
+      return (
+        <Flex justify="flex-start">
+          <HStack align="flex-start" maxW="75%">
+            <Avatar size="sm" bg="red.500" icon={<FiCpu fontSize="1rem" />} />
+            <Box
+              bg="red.50"
+              color="red.700"
+              px={4}
+              py={3}
+              borderRadius="lg"
+              borderWidth="1px"
+              borderColor="red.200"
+            >
+              <Text fontSize="sm">{message.content}</Text>
+            </Box>
+          </HStack>
+        </Flex>
+      );
+
+    default:
+      return null;
+  }
+};
+
+export default ChatInterfaceV2;
--- a/src/components/ChatBot/MessageBubble.js
+++ b/src/components/ChatBot/MessageBubble.js
@@ -0,0 +1,149 @@
+// src/components/ChatBot/MessageBubble.js
+// 聊天消息气泡组件
+
+import React from 'react';
+import {
+  Box,
+  Flex,
+  Text,
+  Avatar,
+  useColorModeValue,
+  IconButton,
+  HStack,
+  Code,
+  Badge,
+  VStack,
+} from '@chakra-ui/react';
+import { FiCopy, FiThumbsUp, FiThumbsDown } from 'react-icons/fi';
+import ReactMarkdown from 'react-markdown';
+
+/**
+ * 消息气泡组件
+ * @param {Object} props
+ * @param {Object} props.message - 消息对象
+ * @param {boolean} props.isUser - 是否是用户消息
+ * @param {Function} props.onCopy - 复制消息回调
+ * @param {Function} props.onFeedback - 反馈回调
+ */
+export const MessageBubble = ({ message, isUser, onCopy, onFeedback }) => {
+  const userBg = useColorModeValue('blue.500', 'blue.600');
+  const botBg = useColorModeValue('gray.100', 'gray.700');
+  const userColor = 'white';
+  const botColor = useColorModeValue('gray.800', 'white');
+
+  const handleCopy = () => {
+    navigator.clipboard.writeText(message.content);
+    onCopy?.();
+  };
+
+  return (
+    <Flex
+      w="100%"
+      justify={isUser ? 'flex-end' : 'flex-start'}
+      mb={4}
+    >
+      <Flex
+        maxW="75%"
+        flexDirection={isUser ? 'row-reverse' : 'row'}
+        align="flex-start"
+      >
+        {/* 头像 */}
+        <Avatar
+          size="sm"
+          name={isUser ? '用户' : 'AI助手'}
+          bg={isUser ? 'blue.500' : 'green.500'}
+          color="white"
+          mx={3}
+        />
+
+        {/* 消息内容 */}
+        <Box>
+          <Box
+            bg={isUser ? userBg : botBg}
+            color={isUser ? userColor : botColor}
+            px={4}
+            py={3}
+            borderRadius="lg"
+            boxShadow="sm"
+          >
+            {message.type === 'text' ? (
+              <Text fontSize="sm" whiteSpace="pre-wrap">
+                {message.content}
+              </Text>
+            ) : message.type === 'markdown' ? (
+              <Box fontSize="sm" className="markdown-content">
+                <ReactMarkdown>{message.content}</ReactMarkdown>
+              </Box>
+            ) : message.type === 'data' ? (
+              <VStack align="stretch" spacing={2}>
+                {message.data && Array.isArray(message.data) && message.data.slice(0, 5).map((item, idx) => (
+                  <Box
+                    key={idx}
+                    p={3}
+                    bg={useColorModeValue('white', 'gray.600')}
+                    borderRadius="md"
+                    fontSize="xs"
+                  >
+                    {Object.entries(item).map(([key, value]) => (
+                      <Flex key={key} justify="space-between" mb={1}>
+                        <Text fontWeight="bold" mr={2}>{key}:</Text>
+                        <Text>{String(value)}</Text>
+                      </Flex>
+                    ))}
+                  </Box>
+                ))}
+                {message.data && message.data.length > 5 && (
+                  <Badge colorScheme="blue" alignSelf="center">
+                    +{message.data.length - 5} 更多结果
+                  </Badge>
+                )}
+              </VStack>
+            ) : null}
+          </Box>
+
+          {/* 消息操作按钮（仅AI消息） */}
+          {!isUser && (
+            <HStack mt={2} spacing={2}>
+              <IconButton
+                icon={<FiCopy />}
+                size="xs"
+                variant="ghost"
+                aria-label="复制"
+                onClick={handleCopy}
+              />
+              <IconButton
+                icon={<FiThumbsUp />}
+                size="xs"
+                variant="ghost"
+                aria-label="赞"
+                onClick={() => onFeedback?.('positive')}
+              />
+              <IconButton
+                icon={<FiThumbsDown />}
+                size="xs"
+                variant="ghost"
+                aria-label="踩"
+                onClick={() => onFeedback?.('negative')}
+              />
+            </HStack>
+          )}
+
+          {/* 时间戳 */}
+          <Text
+            fontSize="xs"
+            color="gray.500"
+            mt={1}
+            textAlign={isUser ? 'right' : 'left'}
+          >
+            {message.timestamp ? new Date(message.timestamp).toLocaleTimeString('zh-CN', {
+              hour: '2-digit',
+              minute: '2-digit',
+            }) : ''}
+          </Text>
+        </Box>
+      </Flex>
+    </Flex>
+  );
+};
+
+export default MessageBubble;
--- a/src/components/ChatBot/PlanCard.js
+++ b/src/components/ChatBot/PlanCard.js
@@ -0,0 +1,145 @@
+// src/components/ChatBot/PlanCard.js
+// 执行计划展示卡片
+
+import React from 'react';
+import {
+  Box,
+  VStack,
+  HStack,
+  Text,
+  Badge,
+  Accordion,
+  AccordionItem,
+  AccordionButton,
+  AccordionPanel,
+  AccordionIcon,
+  Icon,
+  useColorModeValue,
+  Divider,
+} from '@chakra-ui/react';
+import { FiTarget, FiCheckCircle, FiXCircle, FiClock, FiTool } from 'react-icons/fi';
+
+/**
+ * 执行计划卡片组件
+ */
+export const PlanCard = ({ plan, stepResults }) => {
+  const cardBg = useColorModeValue('blue.50', 'blue.900');
+  const borderColor = useColorModeValue('blue.200', 'blue.700');
+  const successColor = useColorModeValue('green.500', 'green.300');
+  const errorColor = useColorModeValue('red.500', 'red.300');
+  const pendingColor = useColorModeValue('gray.400', 'gray.500');
+
+  const getStepStatus = (stepIndex) => {
+    if (!stepResults || stepResults.length === 0) return 'pending';
+    const result = stepResults.find(r => r.step_index === stepIndex);
+    return result ? result.status : 'pending';
+  };
+
+  const getStepIcon = (status) => {
+    switch (status) {
+      case 'success':
+        return FiCheckCircle;
+      case 'failed':
+        return FiXCircle;
+      default:
+        return FiClock;
+    }
+  };
+
+  const getStepColor = (status) => {
+    switch (status) {
+      case 'success':
+        return successColor;
+      case 'failed':
+        return errorColor;
+      default:
+        return pendingColor;
+    }
+  };
+
+  return (
+    <Box
+      bg={cardBg}
+      borderRadius="lg"
+      borderWidth="2px"
+      borderColor={borderColor}
+      p={4}
+      mb={4}
+      boxShadow="md"
+    >
+      <VStack align="stretch" spacing={3}>
+        {/* 目标 */}
+        <HStack>
+          <Icon as={FiTarget} color="blue.500" boxSize={5} />
+          <Text fontWeight="bold" fontSize="md">执行目标</Text>
+        </HStack>
+        <Text fontSize="sm" color="gray.600" pl={7}>
+          {plan.goal}
+        </Text>
+
+        <Divider />
+
+        {/* 规划思路 */}
+        {plan.reasoning && (
+          <>
+            <Text fontSize="sm" fontWeight="bold">规划思路：</Text>
+            <Text fontSize="sm" color="gray.600">
+              {plan.reasoning}
+            </Text>
+            <Divider />
+          </>
+        )}
+
+        {/* 执行步骤 */}
+        <HStack justify="space-between">
+          <Text fontSize="sm" fontWeight="bold">执行步骤</Text>
+          <Badge colorScheme="blue">{plan.steps.length} 步</Badge>
+        </HStack>
+
+        <VStack align="stretch" spacing={2}>
+          {plan.steps.map((step, index) => {
+            const status = getStepStatus(index);
+            const StepIcon = getStepIcon(status);
+            const stepColor = getStepColor(status);
+
+            return (
+              <HStack
+                key={index}
+                p={2}
+                bg={useColorModeValue('white', 'gray.700')}
+                borderRadius="md"
+                borderWidth="1px"
+                borderColor={stepColor}
+                align="flex-start"
+              >
+                <Icon as={StepIcon} color={stepColor} boxSize={4} mt={1} />
+                <VStack align="stretch" flex={1} spacing={1}>
+                  <HStack justify="space-between">
+                    <Text fontSize="sm" fontWeight="bold">
+                      步骤 {index + 1}: {step.tool}
+                    </Text>
+                    <Badge
+                      colorScheme={
+                        status === 'success' ? 'green' :
+                        status === 'failed' ? 'red' : 'gray'
+                      }
+                      fontSize="xs"
+                    >
+                      {status === 'success' ? '✓ 完成' :
+                       status === 'failed' ? '✗ 失败' : '⏳ 等待'}
+                    </Badge>
+                  </HStack>
+                  <Text fontSize="xs" color="gray.600">
+                    {step.reason}
+                  </Text>
+                </VStack>
+              </HStack>
+            );
+          })}
+        </VStack>
+      </VStack>
+    </Box>
+  );
+};
+
+export default PlanCard;
--- a/src/components/ChatBot/StepResultCard.js
+++ b/src/components/ChatBot/StepResultCard.js
@@ -0,0 +1,186 @@
+// src/components/ChatBot/StepResultCard.js
+// 步骤结果展示卡片（可折叠）
+
+import React, { useState } from 'react';
+import {
+  Box,
+  VStack,
+  HStack,
+  Text,
+  Badge,
+  Collapse,
+  Icon,
+  IconButton,
+  Code,
+  useColorModeValue,
+  Divider,
+} from '@chakra-ui/react';
+import { FiChevronDown, FiChevronUp, FiCheckCircle, FiXCircle, FiClock, FiDatabase } from 'react-icons/fi';
+
+/**
+ * 步骤结果卡片组件
+ */
+export const StepResultCard = ({ stepResult }) => {
+  const [isExpanded, setIsExpanded] = useState(false);
+
+  const cardBg = useColorModeValue('white', 'gray.700');
+  const borderColor = useColorModeValue('gray.200', 'gray.600');
+  const successColor = useColorModeValue('green.500', 'green.300');
+  const errorColor = useColorModeValue('red.500', 'red.300');
+
+  const getStatusIcon = () => {
+    switch (stepResult.status) {
+      case 'success':
+        return FiCheckCircle;
+      case 'failed':
+        return FiXCircle;
+      default:
+        return FiClock;
+    }
+  };
+
+  const getStatusColor = () => {
+    switch (stepResult.status) {
+      case 'success':
+        return 'green';
+      case 'failed':
+        return 'red';
+      default:
+        return 'gray';
+    }
+  };
+
+  const StatusIcon = getStatusIcon();
+  const statusColorScheme = getStatusColor();
+
+  // 格式化数据以便展示
+  const formatResult = (data) => {
+    if (typeof data === 'string') return data;
+    if (Array.isArray(data)) {
+      return `找到 ${data.length} 条记录`;
+    }
+    if (typeof data === 'object') {
+      return JSON.stringify(data, null, 2);
+    }
+    return String(data);
+  };
+
+  return (
+    <Box
+      bg={cardBg}
+      borderRadius="md"
+      borderWidth="1px"
+      borderColor={borderColor}
+      overflow="hidden"
+      boxShadow="sm"
+    >
+      {/* 头部 - 始终可见 */}
+      <HStack
+        p={3}
+        justify="space-between"
+        cursor="pointer"
+        onClick={() => setIsExpanded(!isExpanded)}
+        _hover={{ bg: useColorModeValue('gray.50', 'gray.600') }}
+      >
+        <HStack flex={1}>
+          <Icon as={StatusIcon} color={`${statusColorScheme}.500`} boxSize={5} />
+          <VStack align="stretch" spacing={0} flex={1}>
+            <HStack>
+              <Text fontSize="sm" fontWeight="bold">
+                步骤 {stepResult.step_index + 1}: {stepResult.tool}
+              </Text>
+              <Badge colorScheme={statusColorScheme} fontSize="xs">
+                {stepResult.status === 'success' ? '成功' :
+                 stepResult.status === 'failed' ? '失败' : '执行中'}
+              </Badge>
+            </HStack>
+            <Text fontSize="xs" color="gray.500">
+              耗时: {stepResult.execution_time?.toFixed(2)}s
+            </Text>
+          </VStack>
+        </HStack>
+
+        <IconButton
+          icon={<Icon as={isExpanded ? FiChevronUp : FiChevronDown} />}
+          size="sm"
+          variant="ghost"
+          aria-label={isExpanded ? "收起" : "展开"}
+        />
+      </HStack>
+
+      {/* 内容 - 可折叠 */}
+      <Collapse in={isExpanded} animateOpacity>
+        <Box p={3} pt={0}>
+          <Divider mb={3} />
+
+          {/* 参数 */}
+          {stepResult.arguments && Object.keys(stepResult.arguments).length > 0 && (
+            <VStack align="stretch" spacing={2} mb={3}>
+              <HStack>
+                <Icon as={FiDatabase} color="blue.500" boxSize={4} />
+                <Text fontSize="xs" fontWeight="bold">请求参数:</Text>
+              </HStack>
+              <Code
+                p={2}
+                borderRadius="md"
+                fontSize="xs"
+                whiteSpace="pre-wrap"
+                wordBreak="break-word"
+              >
+                {JSON.stringify(stepResult.arguments, null, 2)}
+              </Code>
+            </VStack>
+          )}
+
+          {/* 结果或错误 */}
+          {stepResult.status === 'success' && stepResult.result && (
+            <VStack align="stretch" spacing={2}>
+              <Text fontSize="xs" fontWeight="bold">执行结果:</Text>
+              <Box
+                maxH="300px"
+                overflowY="auto"
+                p={2}
+                bg={useColorModeValue('gray.50', 'gray.800')}
+                borderRadius="md"
+                fontSize="xs"
+              >
+                {typeof stepResult.result === 'string' ? (
+                  <Text whiteSpace="pre-wrap">{stepResult.result}</Text>
+                ) : Array.isArray(stepResult.result) ? (
+                  <VStack align="stretch" spacing={2}>
+                    <Text fontWeight="bold">找到 {stepResult.result.length} 条记录:</Text>
+                    {stepResult.result.slice(0, 3).map((item, idx) => (
+                      <Code key={idx} p={2} borderRadius="md" fontSize="xs">
+                        {JSON.stringify(item, null, 2)}
+                      </Code>
+                    ))}
+                    {stepResult.result.length > 3 && (
+                      <Text fontSize="xs" color="gray.500">
+                        ...还有 {stepResult.result.length - 3} 条记录
+                      </Text>
+                    )}
+                  </VStack>
+                ) : (
+                  <Code whiteSpace="pre-wrap" wordBreak="break-word">
+                    {JSON.stringify(stepResult.result, null, 2)}
+                  </Code>
+                )}
+              </Box>
+            </VStack>
+          )}
+
+          {stepResult.status === 'failed' && stepResult.error && (
+            <VStack align="stretch" spacing={2}>
+              <Text fontSize="xs" fontWeight="bold" color="red.500">错误信息:</Text>
+              <Text fontSize="xs" color="red.600" p={2} bg="red.50" borderRadius="md">
+                {stepResult.error}
+              </Text>
+            </VStack>
+          )}
+        </Box>
+      </Collapse>
+    </Box>
+  );
+};
+
+export default StepResultCard;
--- a/src/components/ChatBot/index.js
+++ b/src/components/ChatBot/index.js
@@ -0,0 +1,11 @@
+// src/components/ChatBot/index.js
+// 聊天机器人组件统一导出
+
+export { ChatInterface } from './ChatInterface';
+export { ChatInterfaceV2 } from './ChatInterfaceV2';
+export { MessageBubble } from './MessageBubble';
+export { PlanCard } from './PlanCard';
+export { StepResultCard } from './StepResultCard';
+
+// 默认导出新版本
+export { ChatInterfaceV2 as default } from './ChatInterfaceV2';
--- a/src/components/Navbars/components/MobileDrawer/MobileDrawer.js
+++ b/src/components/Navbars/components/MobileDrawer/MobileDrawer.js
@@ -243,6 +243,26 @@ const MobileDrawer = memo(({
                        <Box>
                            <Text fontWeight="bold" mb={2}>AGENT社群</Text>
                            <VStack spacing={2} align="stretch">
+                                <Link
+                                    onClick={() => handleNavigate('/agent-chat')}
+                                    py={1}
+                                    px={3}
+                                    borderRadius="md"
+                                    _hover={{ bg: 'gray.100' }}
+                                    cursor="pointer"
+                                    bg={location.pathname.includes('/agent-chat') ? 'blue.50' : 'transparent'}
+                                    borderLeft={location.pathname.includes('/agent-chat') ? '3px solid' : 'none'}
+                                    borderColor="blue.600"
+                                    fontWeight={location.pathname.includes('/agent-chat') ? 'bold' : 'normal'}
+                                >
+                                    <HStack justify="space-between">
+                                        <Text fontSize="sm">AI聊天助手</Text>
+                                        <HStack spacing={1}>
+                                            <Badge size="xs" colorScheme="green">AI</Badge>
+                                            <Badge size="xs" colorScheme="red">NEW</Badge>
+                                        </HStack>
+                                    </HStack>
+                                </Link>
                                <Link
                                    py={1}
                                    px={3}
--- a/src/components/Navbars/components/Navigation/DesktopNav.js
+++ b/src/components/Navbars/components/Navigation/DesktopNav.js
@@ -199,6 +199,12 @@ const DesktopNav = memo(({ isAuthenticated, user }) => {
                    as={Button}
                    variant="ghost"
                    rightIcon={<ChevronDownIcon />}
+                    bg={isActive(['/agent-chat']) ? 'blue.50' : 'transparent'}
+                    color={isActive(['/agent-chat']) ? 'blue.600' : 'inherit'}
+                    fontWeight={isActive(['/agent-chat']) ? 'bold' : 'normal'}
+                    borderBottom={isActive(['/agent-chat']) ? '2px solid' : 'none'}
+                    borderColor="blue.600"
+                    _hover={{ bg: isActive(['/agent-chat']) ? 'blue.100' : 'gray.50' }}
                    onMouseEnter={agentCommunityMenu.handleMouseEnter}
                    onMouseLeave={agentCommunityMenu.handleMouseLeave}
                    onClick={agentCommunityMenu.handleClick}
@@ -207,10 +213,31 @@ const DesktopNav = memo(({ isAuthenticated, user }) => {
                </MenuButton>
                <MenuList
                    minW="300px"
-                    p={4}
+                    p={2}
                    onMouseEnter={agentCommunityMenu.handleMouseEnter}
                    onMouseLeave={agentCommunityMenu.handleMouseLeave}
                >
+                    <MenuItem
+                        onClick={() => {
+                            // 🎯 追踪菜单项点击
+                            navEvents.trackMenuItemClicked('AI聊天助手', 'dropdown', '/agent-chat');
+                            navigate('/agent-chat');
+                            agentCommunityMenu.onClose(); // 跳转后关闭菜单
+                        }}
+                        borderRadius="md"
+                        bg={location.pathname.includes('/agent-chat') ? 'blue.50' : 'transparent'}
+                        borderLeft={location.pathname.includes('/agent-chat') ? '3px solid' : 'none'}
+                        borderColor="blue.600"
+                        fontWeight={location.pathname.includes('/agent-chat') ? 'bold' : 'normal'}
+                    >
+                        <Flex justify="space-between" align="center" w="100%">
+                            <Text fontSize="sm">AI聊天助手</Text>
+                            <HStack spacing={1}>
+                                <Badge size="sm" colorScheme="green">AI</Badge>
+                                <Badge size="sm" colorScheme="red">NEW</Badge>
+                            </HStack>
+                        </Flex>
+                    </MenuItem>
                    <MenuItem
                        isDisabled
                        cursor="not-allowed"
--- a/src/components/Navbars/components/Navigation/MoreMenu.js
+++ b/src/components/Navbars/components/Navigation/MoreMenu.js
@@ -139,6 +139,22 @@ const MoreMenu = memo(({ isAuthenticated, user }) => {

                {/* AGENT社群组 */}
                <Text fontSize="xs" fontWeight="bold" px={3} py={2} color="gray.500">AGENT社群</Text>
+                <MenuItem
+                    onClick={() => {
+                        moreMenu.onClose(); // 先关闭菜单
+                        navigate('/agent-chat');
+                    }}
+                    borderRadius="md"
+                    bg={location.pathname.includes('/agent-chat') ? 'blue.50' : 'transparent'}
+                >
+                    <Flex justify="space-between" align="center" w="100%">
+                        <Text fontSize="sm">AI聊天助手</Text>
+                        <HStack spacing={1}>
+                            <Badge size="sm" colorScheme="green">AI</Badge>
+                            <Badge size="sm" colorScheme="red">NEW</Badge>
+                        </HStack>
+                    </Flex>
+                </MenuItem>
                <MenuItem isDisabled cursor="not-allowed" color="gray.400">
                    <Text fontSize="sm" color="gray.400">今日热议</Text>
                </MenuItem>
--- a/src/routes/lazy-components.js
+++ b/src/routes/lazy-components.js
@@ -35,6 +35,9 @@ export const lazyComponents = {
    ForecastReport: React.lazy(() => import('../views/Company/ForecastReport')),
    FinancialPanorama: React.lazy(() => import('../views/Company/FinancialPanorama')),
    MarketDataView: React.lazy(() => import('../views/Company/MarketDataView')),
+
+    // Agent模块
+    AgentChat: React.lazy(() => import('../views/AgentChat')),
 };

 /**
@@ -59,4 +62,5 @@ export const {
    ForecastReport,
    FinancialPanorama,
    MarketDataView,
+    AgentChat,
 } = lazyComponents;
--- a/src/routes/routeConfig.js
+++ b/src/routes/routeConfig.js
@@ -149,6 +149,18 @@ export const routeConfig = [
            description: '实时市场数据'
        }
    },
+
+    // ==================== Agent模块 ====================
+    {
+        path: 'agent-chat',
+        component: lazyComponents.AgentChat,
+        protection: PROTECTION_MODES.MODAL,
+        layout: 'main',
+        meta: {
+            title: 'AI投资助手',
+            description: '基于MCP的智能投资顾问'
+        }
+    },
 ];

 /**
--- a/src/services/llmService.js
+++ b/src/services/llmService.js
@@ -0,0 +1,278 @@
+// src/services/llmService.js
+// LLM服务层 - 集成AI模型进行对话和工具调用
+
+import axios from 'axios';
+import { mcpService } from './mcpService';
+import { logger } from '../utils/logger';
+
+/**
+ * LLM服务配置
+ */
+const LLM_CONFIG = {
+  // 可以使用 OpenAI、Claude、通义千问等
+  provider: 'openai', // 或 'claude', 'qwen'
+  apiKey: process.env.REACT_APP_OPENAI_API_KEY || '',
+  apiUrl: 'https://api.openai.com/v1/chat/completions',
+  model: 'gpt-4o-mini', // 更便宜的模型
+};
+
+/**
+ * LLM服务类
+ */
+class LLMService {
+  constructor() {
+    this.conversationHistory = [];
+  }
+
+  /**
+   * 构建系统提示词
+   */
+  getSystemPrompt(availableTools) {
+    return `你是一个专业的金融投资助手。你可以使用以下工具来帮助用户查询信息：
+
+${availableTools.map(tool => `
+**${tool.name}**
+描述：${tool.description}
+参数：${JSON.stringify(tool.parameters, null, 2)}
+`).join('\n')}
+
+用户提问时，请按照以下步骤：
+1. 理解用户的意图
+2. 选择合适的工具（可以多个）
+3. 提取工具需要的参数
+4. 调用工具后，用自然语言总结结果
+
+回复格式：
+- 如果需要调用工具，返回JSON格式：{"tool": "工具名", "arguments": {...}}
+- 如果不需要工具，直接回复自然语言
+
+注意：
+- 贵州茅台的股票代码是 600519
+- 涨停是指股票当日涨幅达到10%
+- 概念板块是指相同题材的股票分类`;
+  }
+
+  /**
+   * 智能对话 - 使用LLM理解意图并调用工具
+   */
+  async chat(userMessage, conversationHistory = []) {
+    try {
+      // 1. 获取可用工具列表
+      const toolsResult = await mcpService.listTools();
+      if (!toolsResult.success) {
+        throw new Error('获取工具列表失败');
+      }
+
+      const availableTools = toolsResult.data;
+
+      // 2. 构建对话历史
+      const messages = [
+        {
+          role: 'system',
+          content: this.getSystemPrompt(availableTools),
+        },
+        ...conversationHistory.map(msg => ({
+          role: msg.isUser ? 'user' : 'assistant',
+          content: msg.content,
+        })),
+        {
+          role: 'user',
+          content: userMessage,
+        },
+      ];
+
+      // 3. 调用LLM
+      logger.info('LLMService', '调用LLM', { messageCount: messages.length });
+
+      // 注意：这里需要配置API密钥
+      if (!LLM_CONFIG.apiKey) {
+        // 如果没有配置LLM，使用简单的关键词匹配
+        logger.warn('LLMService', '未配置LLM API密钥，使用简单匹配');
+        return await this.fallbackChat(userMessage);
+      }
+
+      const response = await axios.post(
+        LLM_CONFIG.apiUrl,
+        {
+          model: LLM_CONFIG.model,
+          messages: messages,
+          temperature: 0.7,
+          max_tokens: 1000,
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json',
+            'Authorization': `Bearer ${LLM_CONFIG.apiKey}`,
+          },
+          timeout: 30000,
+        }
+      );
+
+      const aiResponse = response.data.choices[0].message.content;
+      logger.info('LLMService', 'LLM响应', { response: aiResponse });
+
+      // 4. 解析LLM响应
+      // 如果LLM返回工具调用指令
+      try {
+        const toolCall = JSON.parse(aiResponse);
+        if (toolCall.tool && toolCall.arguments) {
+          // 调用MCP工具
+          const toolResult = await mcpService.callTool(toolCall.tool, toolCall.arguments);
+
+          if (!toolResult.success) {
+            return {
+              success: false,
+              error: toolResult.error,
+            };
+          }
+
+          // 5. 让LLM总结工具结果
+          const summaryMessages = [
+            ...messages,
+            {
+              role: 'assistant',
+              content: aiResponse,
+            },
+            {
+              role: 'system',
+              content: `工具 ${toolCall.tool} 返回的数据：\n${JSON.stringify(toolResult.data, null, 2)}\n\n请用自然语言总结这些数据，给用户一个简洁清晰的回复。`,
+            },
+          ];
+
+          const summaryResponse = await axios.post(
+            LLM_CONFIG.apiUrl,
+            {
+              model: LLM_CONFIG.model,
+              messages: summaryMessages,
+              temperature: 0.7,
+              max_tokens: 500,
+            },
+            {
+              headers: {
+                'Content-Type': 'application/json',
+                'Authorization': `Bearer ${LLM_CONFIG.apiKey}`,
+              },
+              timeout: 30000,
+            }
+          );
+
+          const summary = summaryResponse.data.choices[0].message.content;
+
+          return {
+            success: true,
+            data: {
+              message: summary,
+              rawData: toolResult.data,
+              toolUsed: toolCall.tool,
+            },
+          };
+        }
+      } catch (parseError) {
+        // 不是JSON格式，说明是直接回复
+        return {
+          success: true,
+          data: {
+            message: aiResponse,
+          },
+        };
+      }
+
+      // 默认返回LLM的直接回复
+      return {
+        success: true,
+        data: {
+          message: aiResponse,
+        },
+      };
+    } catch (error) {
+      logger.error('LLMService', 'chat error', error);
+      return {
+        success: false,
+        error: error.message || '对话处理失败',
+      };
+    }
+  }
+
+  /**
+   * 降级方案：简单的关键词匹配（当没有配置LLM时）
+   */
+  async fallbackChat(userMessage) {
+    logger.info('LLMService', '使用降级方案', { message: userMessage });
+
+    // 使用原有的简单匹配逻辑
+    if (userMessage.includes('新闻') || userMessage.includes('资讯')) {
+      const result = await mcpService.callTool('search_china_news', {
+        query: userMessage.replace(/新闻|资讯/g, '').trim(),
+        top_k: 5,
+      });
+      return this.formatFallbackResponse(result, '新闻搜索');
+    } else if (userMessage.includes('概念') || userMessage.includes('板块')) {
+      const query = userMessage.replace(/概念|板块/g, '').trim();
+      const result = await mcpService.callTool('search_concepts', {
+        query,
+        size: 5,
+        sort_by: 'change_pct',
+      });
+      return this.formatFallbackResponse(result, '概念搜索');
+    } else if (userMessage.includes('涨停')) {
+      const query = userMessage.replace(/涨停/g, '').trim();
+      const result = await mcpService.callTool('search_limit_up_stocks', {
+        query,
+        mode: 'hybrid',
+        page_size: 5,
+      });
+      return this.formatFallbackResponse(result, '涨停分析');
+    } else if (/^[0-9]{6}$/.test(userMessage.trim())) {
+      // 6位数字 = 股票代码
+      const result = await mcpService.callTool('get_stock_basic_info', {
+        seccode: userMessage.trim(),
+      });
+      return this.formatFallbackResponse(result, '股票信息');
+    } else if (userMessage.includes('茅台') || userMessage.includes('贵州茅台')) {
+      // 特殊处理茅台
+      const result = await mcpService.callTool('get_stock_basic_info', {
+        seccode: '600519',
+      });
+      return this.formatFallbackResponse(result, '贵州茅台股票信息');
+    } else {
+      // 默认：搜索新闻
+      const result = await mcpService.callTool('search_china_news', {
+        query: userMessage,
+        top_k: 5,
+      });
+      return this.formatFallbackResponse(result, '新闻搜索');
+    }
+  }
+
+  /**
+   * 格式化降级响应
+   */
+  formatFallbackResponse(result, action) {
+    if (!result.success) {
+      return {
+        success: false,
+        error: result.error,
+      };
+    }
+
+    return {
+      success: true,
+      data: {
+        message: `已为您完成${action}，找到以下结果：`,
+        rawData: result.data,
+      },
+    };
+  }
+
+  /**
+   * 清除对话历史
+   */
+  clearHistory() {
+    this.conversationHistory = [];
+  }
+}
+
+// 导出单例
+export const llmService = new LLMService();
+
+export default LLMService;
--- a/src/services/mcpService.js
+++ b/src/services/mcpService.js
@@ -0,0 +1,248 @@
+// src/services/mcpService.js
+// MCP (Model Context Protocol) 服务层
+// 用于与FastAPI后端的MCP工具进行交互
+
+import axios from 'axios';
+import { getApiBase } from '../utils/apiConfig';
+import { logger } from '../utils/logger';
+
+/**
+ * MCP API客户端
+ */
+class MCPService {
+  constructor() {
+    this.baseURL = `${getApiBase()}/mcp`;
+    this.client = axios.create({
+      baseURL: this.baseURL,
+      timeout: 60000, // 60秒超时（MCP工具可能需要较长时间）
+      headers: {
+        'Content-Type': 'application/json',
+      },
+    });
+
+    // 请求拦截器
+    this.client.interceptors.request.use(
+      (config) => {
+        logger.debug('MCPService', 'Request', {
+          url: config.url,
+          method: config.method,
+          data: config.data,
+        });
+        return config;
+      },
+      (error) => {
+        logger.error('MCPService', 'Request Error', error);
+        return Promise.reject(error);
+      }
+    );
+
+    // 响应拦截器
+    this.client.interceptors.response.use(
+      (response) => {
+        logger.debug('MCPService', 'Response', {
+          url: response.config.url,
+          status: response.status,
+          data: response.data,
+        });
+        return response.data;
+      },
+      (error) => {
+        logger.error('MCPService', 'Response Error', {
+          url: error.config?.url,
+          status: error.response?.status,
+          message: error.message,
+        });
+        return Promise.reject(error);
+      }
+    );
+  }
+
+  /**
+   * 列出所有可用的MCP工具
+   * @returns {Promise<Object>} 工具列表
+   */
+  async listTools() {
+    try {
+      const response = await this.client.get('/tools');
+      return {
+        success: true,
+        data: response.tools || [],
+      };
+    } catch (error) {
+      return {
+        success: false,
+        error: error.message || '获取工具列表失败',
+      };
+    }
+  }
+
+  /**
+   * 获取特定工具的定义
+   * @param {string} toolName - 工具名称
+   * @returns {Promise<Object>} 工具定义
+   */
+  async getTool(toolName) {
+    try {
+      const response = await this.client.get(`/tools/${toolName}`);
+      return {
+        success: true,
+        data: response,
+      };
+    } catch (error) {
+      return {
+        success: false,
+        error: error.message || '获取工具定义失败',
+      };
+    }
+  }
+
+  /**
+   * 调用MCP工具
+   * @param {string} toolName - 工具名称
+   * @param {Object} arguments - 工具参数
+   * @returns {Promise<Object>} 工具执行结果
+   */
+  async callTool(toolName, toolArguments) {
+    try {
+      const response = await this.client.post('/tools/call', {
+        tool: toolName,
+        arguments: toolArguments,
+      });
+      return {
+        success: true,
+        data: response.data || response,
+      };
+    } catch (error) {
+      return {
+        success: false,
+        error: error.response?.data?.detail || error.message || '工具调用失败',
+      };
+    }
+  }
+
+  /**
+   * 智能对话 - 根据用户输入自动选择合适的工具
+   * @param {string} userMessage - 用户消息
+   * @param {Array} conversationHistory - 对话历史（可选）
+   * @returns {Promise<Object>} AI响应
+   */
+  async chat(userMessage, conversationHistory = []) {
+    try {
+      // 这里可以实现智能路由逻辑
+      // 根据用户输入判断应该调用哪个工具
+
+      // 示例：关键词匹配
+      if (userMessage.includes('新闻') || userMessage.includes('资讯')) {
+        return await this.callTool('search_china_news', {
+          query: userMessage.replace(/新闻|资讯/g, '').trim(),
+          top_k: 5,
+        });
+      } else if (userMessage.includes('概念') || userMessage.includes('板块')) {
+        const query = userMessage.replace(/概念|板块/g, '').trim();
+        return await this.callTool('search_concepts', {
+          query,
+          size: 5,
+          sort_by: 'change_pct',
+        });
+      } else if (userMessage.includes('涨停')) {
+        const query = userMessage.replace(/涨停/g, '').trim();
+        return await this.callTool('search_limit_up_stocks', {
+          query,
+          mode: 'hybrid',
+          page_size: 5,
+        });
+      } else if (/^[0-9]{6}$/.test(userMessage.trim())) {
+        // 6位数字 = 股票代码
+        return await this.callTool('get_stock_basic_info', {
+          seccode: userMessage.trim(),
+        });
+      } else {
+        // 默认：搜索新闻
+        return await this.callTool('search_china_news', {
+          query: userMessage,
+          top_k: 5,
+        });
+      }
+    } catch (error) {
+      return {
+        success: false,
+        error: error.message || '对话处理失败',
+      };
+    }
+  }
+
+  /**
+   * 工具类别枚举
+   */
+  static TOOL_CATEGORIES = {
+    NEWS: 'news', // 新闻搜索
+    STOCK: 'stock', // 股票信息
+    CONCEPT: 'concept', // 概念板块
+    LIMIT_UP: 'limit_up', // 涨停分析
+    RESEARCH: 'research', // 研报搜索
+    ROADSHOW: 'roadshow', // 路演信息
+    FINANCIAL: 'financial', // 财务数据
+    TRADE: 'trade', // 交易数据
+  };
+
+  /**
+   * 常用工具快捷方式
+   */
+  async searchNews(query, topK = 5, exactMatch = false) {
+    return await this.callTool('search_china_news', {
+      query,
+      top_k: topK,
+      exact_match: exactMatch,
+    });
+  }
+
+  async searchConcepts(query, size = 10, sortBy = 'change_pct') {
+    return await this.callTool('search_concepts', {
+      query,
+      size,
+      sort_by: sortBy,
+    });
+  }
+
+  async searchLimitUpStocks(query, mode = 'hybrid', pageSize = 10) {
+    return await this.callTool('search_limit_up_stocks', {
+      query,
+      mode,
+      page_size: pageSize,
+    });
+  }
+
+  async getStockInfo(seccode) {
+    return await this.callTool('get_stock_basic_info', {
+      seccode,
+    });
+  }
+
+  async getStockConcepts(stockCode, size = 10) {
+    return await this.callTool('get_stock_concepts', {
+      stock_code: stockCode,
+      size,
+    });
+  }
+
+  async searchResearchReports(query, mode = 'hybrid', size = 5) {
+    return await this.callTool('search_research_reports', {
+      query,
+      mode,
+      size,
+    });
+  }
+
+  async getConceptStatistics(days = 7, minStockCount = 3) {
+    return await this.callTool('get_concept_statistics', {
+      days,
+      min_stock_count: minStockCount,
+    });
+  }
+}
+
+// 导出单例实例
+export const mcpService = new MCPService();
+
+// 导出类（供测试使用）
+export default MCPService;
--- a/src/views/AgentChat/index.js
+++ b/src/views/AgentChat/index.js
@@ -0,0 +1,53 @@
+// src/views/AgentChat/index.js
+// Agent聊天页面
+
+import React from 'react';
+import {
+  Box,
+  Container,
+  Heading,
+  Text,
+  VStack,
+  useColorModeValue,
+} from '@chakra-ui/react';
+import { ChatInterfaceV2 } from '../../components/ChatBot';
+
+/**
+ * Agent聊天页面
+ * 提供基于MCP的AI助手对话功能
+ */
+const AgentChat = () => {
+  const bgColor = useColorModeValue('gray.50', 'gray.900');
+  const cardBg = useColorModeValue('white', 'gray.800');
+
+  return (
+    <Box minH="calc(100vh - 200px)" bg={bgColor} py={8}>
+      <Container maxW="container.xl" h="100%">
+        <VStack spacing={6} align="stretch" h="100%">
+          {/* 页面标题 */}
+          <Box>
+            <Heading size="lg" mb={2}>AI投资助手</Heading>
+            <Text color="gray.600" fontSize="sm">
+              基于MCP协议的智能投资顾问，支持股票查询、新闻搜索、概念分析等多种功能
+            </Text>
+          </Box>
+
+          {/* 聊天界面 */}
+          <Box
+            flex="1"
+            bg={cardBg}
+            borderRadius="xl"
+            boxShadow="xl"
+            overflow="hidden"
+            h="calc(100vh - 300px)"
+            minH="600px"
+          >
+            <ChatInterfaceV2 />
+          </Box>
+        </VStack>
+      </Container>
+    </Box>
+  );
+};
+
+export default AgentChat;