Stock Tool MCP Server

🇨🇳 中文文档

📖 项目简介

本项目旨在为 AI Agent（如 Claude, Cursor, 通义千问等）赋予专业级的股市分析能力，打通大语言模型与实时金融数据之间的桥梁。

通过 MCP (Model Context Protocol) 协议，AI 可以直接调用本服务器提供的金融工具，实现：

• 📊 实时行情查询
• 📈 技术指标计算
• 💰 基本面分析
• 📰 新闻资讯获取
• 🔍 深度研究报告

🚀 核心功能

1. 多源市场数据融合

无需纠结使用哪个 API。本服务器内置智能 Adapter Manager（适配器管理器），可自动路由请求并在多个数据源之间进行故障转移：

• 美股: Yahoo Finance, Finnhub
• A股: Akshare, Tushare, Baostock
• 加密货币: CCXT (Binance, OKX 等)
• 外汇与指数: Yahoo Finance

2. 专业技术分析

内置量化分析引擎，提供的不仅仅是原始数字：

• 技术指标: SMA/EMA, RSI, MACD, 布林带 (Bollinger Bands), KDJ, ATR 等
• 形态识别: 自动检测 K 线形态（如十字星 Doji, 锤头线 Hammer, 吞没形态 Engulfing）
• 支撑与压力: 动态计算关键价格位
• 筹码分布 (Volume Profile): 分析成交量分布以识别价值区域

3. 深度基本面研究

自动化的金融分析师能力：

• 财务报表: 资产负债表、利润表、现金流量表
• 健康度打分: 基于盈利能力、偿债能力、成长性和估值的 0-100 分独家健康度评分
• 关键比率: PE, PB, ROE, ROA, 负债权益比等

4. 智能聚合工具

专为 LLM 上下文窗口优化：

• perform_deep_research: 一键获取指定标的的 价格 + 历史走势 + 基本面 + 近期新闻
• get_market_report: 获取当前市场状态的综合快照

🛠️ 安装指南

前置要求

• Python 3.10+
• Redis (可选，用于缓存)

安装步骤

1. 克隆仓库

   git clone https://github.com/yourusername/stock-tool-mcp.git
   cd stock-tool-mcp
   

2. 创建并激活 Conda 环境

   # 创建 Python 3.11.14 环境
   conda create -n stock-mcp python=3.11.14
   
   # 激活环境
   conda activate stock-mcp
   

3. 安装依赖

   pip install -r requirements.txt
   

4. 配置环境变量

   复制示例环境变量文件:

   cp .env.example .env
   

   编辑 .env 添加你的 API 密钥（可选，但推荐以获得更高限额）:

   • TUSHARE_ENABLED - 是否启用 Tushare 数据源（默认 False）
   • TUSHARE_TOKEN - 用于 A 股数据（获取 Token）
   • FINNHUB_ENABLED - 是否启用 Finnhub 数据源（默认 False）
   • FINNHUB_API_KEY - 用于美股机构数据（获取 API Key）
   • DASHSCOPE_API_KEY - 用于阿里百炼 AI（可选，用于测试）

   💡 提示: 本项目采用可插拔设计。如果没有配置 API Key，系统会自动禁用相应的数据源，并使用免费的替代方案（如 Akshare, Baostock, Yahoo Finance）作为后备。

🏃‍♂️ 使用方法

方式一：作为 HTTP 服务器运行（推荐用于测试和开发）

使用 uvicorn 启动 MCP 服务器（Streamable HTTP 模式）：

# 设置环境变量指定传输方式为 streamable-http
export MCP_TRANSPORT=streamable-http

# 标准启动（监听 9898 端口）
python -m uvicorn src.server.app:app --host 0.0.0.0 --port 9898

# 开发模式（支持热重载）
MCP_TRANSPORT=streamable-http python -m uvicorn src.server.app:app --reload --port 9898

启动成功后，你会看到：

✅ MCP server ready!

使用示例（Streamable HTTP）：

方式二：使用 stdio 模式（推荐用于 AI Agent 集成）

stdio 模式通过标准输入输出与 AI Agent 通信，适合 Claude Desktop、Cursor 等本地集成。

快速启动：

# 使用启动脚本（已配置好 conda 环境）
bash start_stock_mcp_stdio.sh

手动启动：

# 激活 conda 环境
conda activate stock-mcp

# 启动 stdio 模式（默认传输方式）
python -c "import src.server.mcp.server as m; m.create_mcp_server().run(transport='stdio')"

集成到 Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "stock-tools": {
      "command": "bash",
      "args": ["start_stock_mcp_stdio.sh"],
      "cwd": "/path/to/stock-tool-mcp"
    }
  }
}

集成到 Cursor (.cursor/mcp_config.json):

{
  "mcpServers": {
    "stock-tools": {
      "command": "bash",
      "args": ["start_stock_mcp_stdio.sh"],
      "cwd": "/path/to/stock-tool-mcp"
    }
  }
}

使用示例（stdio 模式）：

方式三：通过 HTTP API 调用

服务器启动后，可以通过 HTTP 接口调用（Streamable HTTP 协议）：

# 列出所有可用工具
curl -X POST http://localhost:9898 \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/list",
    "params": {},
    "id": "1"
  }'

# 调用工具示例：查询贵州茅台价格
curl -X POST "http://localhost:9898/?_tool=get_real_time_price" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "get_real_time_price",
      "arguments": {
        "ticker": "SSE:600519"
      }
    },
    "id": "2"
  }'

🧰 可用工具一览

💡 重要提示:

• A股股票代码格式：SSE:600519（上交所）、SZSE:000001（深交所）
• 美股股票代码格式：NASDAQ:AAPL、NYSE:TSLA
• 加密货币格式：CRYPTO:BTC、CRYPTO:ETH

📸 实际使用示例

本项目支持两种传输协议，分别适用于不同场景：

1. Streamable HTTP 模式

适合通过 HTTP 接口调用，方便测试和集成到 Web 应用：

2. stdio 模式

适合直接集成到 AI Agent（如 Claude Desktop、Cursor），通过标准输入输出通信：

💡 两种模式的区别：

• Streamable HTTP: 需要启动 Web 服务器，支持远程调用，适合生产环境
• stdio: 直接进程通信，无需网络端口，适合本地 AI Agent 集成，延迟更低

🧪 测试脚本

项目提供了完整的测试脚本，帮助你快速验证功能：

1. HTTP 接口测试

使用 scripts/test_mcp_http.py 测试 MCP 服务器的 HTTP 接口：

# 1. 启动 MCP 服务器（在一个终端）
python -m uvicorn src.server.app:app --host 0.0.0.0 --port 9898

# 2. 在另一个终端运行测试脚本
python scripts/test_mcp_http.py

该脚本会：

• ✅ 连接到 MCP 服务器（http://localhost:9898）
• ✅ 列出所有可用工具
• ✅ 使用阿里百炼（通义千问）调用工具
• ✅ 查询贵州茅台的价格和基本面

2. OpenAPI 文档生成

使用 scripts/mcp2openapi.py 生成 OpenAPI 规范文档：

python scripts/mcp2openapi.py

生成的 OpenAPI 文档可以导入到 Apifox、Postman 等工具中进行测试。

🗺️ 路线图与未来计划

虽然当前的数据检索和分析能力已相当健壮，但以下功能计划在未来版本中支持：

• 实盘交易执行: 目前 execute_order 工具处于 模拟模式 (Simulation Mode)。我们计划通过 CCXT（加密货币）和券商 API（股票）集成真实的交易下单能力
• 高级缓存策略: 实现更细粒度的 TTL (Time-To-Live) 设置，区分实时价格数据（短 TTL）和财务报表（长 TTL），以平衡性能与 API 配额消耗
• 用户账户管理: 安全地管理用户特定的交易所 API 密钥，实现个性化交易
• 更多数据适配器: 扩展支持更多专业数据源（如情绪分析提供商、另类数据等）
• WebSocket 实时推送: 支持实时行情推送，减少轮询开销
• 回测引擎: 内置策略回测功能，验证交易策略的有效性

🏗️ 项目架构

本项目采用 领域驱动设计 (DDD) 架构，清晰分离关注点：

src/server/
├── app.py                 # FastMCP 应用入口
├── config/                # 配置管理
├── core/                  # 核心业务逻辑
│   └── dependencies.py    # 依赖注入容器
├── domain/                # 领域层
│   ├── adapters/          # 数据适配器（Yahoo, Akshare, Tushare 等）
│   ├── models/            # 领域模型
│   └── services/          # 领域服务
├── infrastructure/        # 基础设施层
│   ├── cache/             # 缓存（Redis）
│   └── external/          # 外部 API 客户端
├── mcp/                   # MCP 协议层
│   └── tools/             # MCP 工具定义
└── utils/                 # 工具类

核心设计原则:

• 📦 适配器模式: 统一多数据源接口，自动故障转移
• 🔌 依赖注入: 使用 dependency-injector 管理服务生命周期
• ⚡ 异步优先: 所有外部调用均为异步，提升并发性能
• 🎯 单一职责: 每个服务专注于特定领域功能

📄 许可证

MIT License

🇬🇧 English Documentation

📖 Introduction

A powerful, comprehensive Model Context Protocol (MCP) server for financial market data, technical analysis, and fundamental research.

Designed to empower AI agents (like Claude, Cursor, etc.) with professional-grade stock market capabilities, bridging the gap between LLMs and real-time financial data.

🚀 Features

1. Multi-Source Market Data

Stop worrying about which API to use. The server features a smart Adapter Manager that automatically routes requests and handles failover across multiple providers:

• US Stocks: Yahoo Finance, Finnhub
• China A-Shares: Akshare, Tushare, Baostock
• Crypto: CCXT (Binance, OKX, etc.)
• Forex & Indices: Yahoo Finance

2. Professional Technical Analysis

Built-in quantitative analysis engine providing more than just raw numbers:

• Indicators: SMA/EMA, RSI, MACD, Bollinger Bands, KDJ, ATR
• Pattern Recognition: Automatically detects candlestick patterns (Doji, Hammer, Engulfing)
• Support & Resistance: Dynamic calculation of key price levels
• Volume Profile: Analysis of volume distribution to identify value areas

3. Deep Fundamental Research

Automated financial analyst capabilities:

• Financial Statements: Balance Sheet, Income Statement, Cash Flow
• Health Scoring: 0-100 proprietary health score based on Profitability, Solvency, Growth, and Valuation
• Key Ratios: PE, PB, ROE, ROA, Debt-to-Equity, and more

4. Smart Aggregation Tools

Optimized for LLM context windows:

• perform_deep_research: One-shot tool to fetch price, history, fundamentals, and recent news for a symbol
• get_market_report: A comprehensive snapshot of the current market status

🛠️ Installation

Prerequisites

• Python 3.10+
• Redis (optional, for caching)

Setup

1. Clone the repository

   git clone https://github.com/yourusername/stock-tool-mcp.git
   cd stock-tool-mcp
   

2. Create and activate Conda environment

   # Create Python 3.11.14 environment
   conda create -n stock-mcp python=3.11.14
   
   # Activate environment
   conda activate stock-mcp
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Configuration

   Copy the example environment file:

   cp .env.example .env
   

   Edit .env to add your API keys (optional but recommended for higher limits):

   • TUSHARE_ENABLED - Enable Tushare data source (default: False)
   • TUSHARE_TOKEN - For China A-shares data (Get Token)
   • FINNHUB_ENABLED - Enable Finnhub data source (default: False)
   • FINNHUB_API_KEY - For US institutional data (Get API Key)
   • DASHSCOPE_API_KEY - For Alibaba Cloud AI (optional, for testing)

   💡 Note: This project features a pluggable design. If API keys are not configured, the system will automatically disable the corresponding data sources and use free alternatives (like Akshare, Baostock, Yahoo Finance) as fallbacks.

🏃‍♂️ Usage

Method 1: Run as HTTP Server (Recommended for Testing & Development)

Start the MCP server using uvicorn (Streamable HTTP mode):

# Set environment variable to specify transport mode
export MCP_TRANSPORT=streamable-http

# Standard run (listening on port 9898)
python -m uvicorn src.server.app:app --host 0.0.0.0 --port 9898

# Development mode (with hot reload)
MCP_TRANSPORT=streamable-http python -m uvicorn src.server.app:app --reload --port 9898

After successful startup, you'll see:

✅ MCP server ready!

Example (Streamable HTTP mode):

Method 2: Use stdio Mode (Recommended for AI Agent Integration)

stdio mode communicates with AI agents via standard input/output, suitable for local integration with Claude Desktop, Cursor, etc.

Quick Start:

# Use the startup script (conda environment pre-configured)
bash start_stock_mcp_stdio.sh

Manual Start:

# Activate conda environment
conda activate stock-mcp

# Start stdio mode (default transport)
python -c "import src.server.mcp.server as m; m.create_mcp_server().run(transport='stdio')"

Integrate with Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "stock-tools": {
      "command": "bash",
      "args": ["start_stock_mcp_stdio.sh"],
      "cwd": "/path/to/stock-tool-mcp"
    }
  }
}

Integrate with Cursor (.cursor/mcp_config.json):

{
  "mcpServers": {
    "stock-tools": {
      "command": "bash",
      "args": ["start_stock_mcp_stdio.sh"],
      "cwd": "/path/to/stock-tool-mcp"
    }
  }
}

Example (stdio mode):

Method 3: HTTP API Calls

After starting the server, you can call it via HTTP interface (Streamable HTTP protocol):

# List all available tools
curl -X POST http://localhost:9898 \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/list",
    "params": {},
    "id": "1"
  }'

# Call tool example: Query Moutai stock price
curl -X POST "http://localhost:9898/?_tool=get_real_time_price" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "get_real_time_price",
      "arguments": {
        "ticker": "SSE:600519"
      }
    },
    "id": "2"
  }'

🧰 Available Tools

💡 Important Note:

• A-share ticker format: SSE:600519 (Shanghai), SZSE:000001 (Shenzhen)
• US stock ticker format: NASDAQ:AAPL, NYSE:TSLA
• Crypto format: CRYPTO:BTC, CRYPTO:ETH

📸 Real-World Examples

This project supports two transport protocols, each suitable for different scenarios:

1. Streamable HTTP Mode

Suitable for HTTP interface calls, convenient for testing and integration into web applications:

2. stdio Mode

Suitable for direct integration into AI Agents (like Claude Desktop, Cursor), communicating via standard input/output:

💡 Differences Between the Two Modes:

• Streamable HTTP: Requires a web server, supports remote calls, suitable for production environments
• stdio: Direct process communication, no network port required, suitable for local AI Agent integration with lower latency

🧪 Test Scripts

The project provides comprehensive test scripts to help you quickly verify functionality:

1. HTTP Interface Testing

Use scripts/test_mcp_http.py to test the MCP server's HTTP interface:

# 1. Start the MCP server (in one terminal)
python -m uvicorn src.server.app:app --host 0.0.0.0 --port 9898

# 2. Run the test script in another terminal
python scripts/test_mcp_http.py

This script will:

• ✅ Connect to the MCP server (http://localhost:9898)
• ✅ List all available tools
• ✅ Use Alibaba Cloud Qwen to call tools
• ✅ Query Moutai's price and fundamentals

2. OpenAPI Documentation Generation

Use scripts/mcp2openapi.py to generate OpenAPI specification:

python scripts/mcp2openapi.py

The generated OpenAPI documentation can be imported into tools like Apifox or Postman for testing.

🗺️ Roadmap & Future Plans

While the data retrieval and analysis capabilities are robust, the following features are planned for future releases:

• Real Trading Execution: Currently, the execute_order tool runs in simulation mode. We plan to integrate real trading capabilities via CCXT (for crypto) and broker APIs (for stocks)
• Advanced Caching Strategy: Implement fine-grained TTL (Time-To-Live) settings to distinguish between real-time price data (short TTL) and financial reports (long TTL) for better performance and API quota management
• User Account Management: Secure handling of user-specific exchange API keys for personalized trading
• More Data Adapters: Expansion to include more specialized data sources (e.g., sentiment analysis providers, alternative data)
• WebSocket Real-time Push: Support real-time market data push to reduce polling overhead
• Backtesting Engine: Built-in strategy backtesting functionality to validate trading strategies

🏗️ Project Architecture

This project adopts Domain-Driven Design (DDD) architecture with clear separation of concerns:

src/server/
├── app.py                 # FastMCP application entry
├── config/                # Configuration management
├── core/                  # Core business logic
│   └── dependencies.py    # Dependency injection container
├── domain/                # Domain layer
│   ├── adapters/          # Data adapters (Yahoo, Akshare, Tushare, etc.)
│   ├── models/            # Domain models
│   └── services/          # Domain services
├── infrastructure/        # Infrastructure layer
│   ├── cache/             # Caching (Redis)
│   └── external/          # External API clients
├── mcp/                   # MCP protocol layer
│   └── tools/             # MCP tool definitions
└── utils/                 # Utilities

Core Design Principles:

• 📦 Adapter Pattern: Unified multi-source interface with automatic failover
• 🔌 Dependency Injection: Using dependency-injector for service lifecycle management
• ⚡ Async First: All external calls are asynchronous for improved concurrency
• 🎯 Single Responsibility: Each service focuses on specific domain functionality

📄 License

MIT License