Finance Intelligence MCP Server

A production-grade, asynchronous Model Context Protocol (MCP) server that grants AI assistants (Claude Desktop, Cursor, ChatGPT, VS Code, etc.) access to real-time financial market data, company metrics, and history.

Powered by FastMCP, yfinance, and Pydantic, this server uses advanced thread-offloading, caching, and robust error isolation to provide standard JSON-RPC tools with zero crashes.

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Architecture Overview

               +--------------------------------------+
               |    LLM Client (e.g. Claude Desktop)  |
               +------------------+-------------------+
                                  |
                                  | (JSON-RPC over stdio / SSE)
                                  v
               +------------------+-------------------+
               |      Finance Intelligence MCP        |
               |                                      |
               |   +------------------------------+   |
               |   |          server.py           |   |
               |   +--------------+---------------+   |
               |                  |                   |
               |                  v                   |
               |   +------------------------------+   |
               |   |   tools (stocks / companies) |   |
               |   +--------------+---------------+   |
               |                  |                   |
               |                  v                   |
               |   +------------------------------+   |
               |   |        clients/yahoo.py      |   |
               |   |   (Thread Pool & TTL Cache)  |   |
               |   +--------------+---------------+   |
               +------------------|-------------------+
                                  |
                                  v (asyncio.to_thread)
               +------------------+-------------------+
               |         Yahoo Finance Service        |
               +--------------------------------------+

Key Design Decisions

1. Async Thread Pool Execution yfinance is fundamentally a blocking synchronous library. Directly calling it in async tool handlers would block the event loop, freezing the MCP server. We offload every yfinance call to a thread pool via asyncio.to_thread.
2. In-Memory TTL Caching To prevent Yahoo Finance rate limits and reduce tool latency during conversational loops, we implement a thread-safe, in-memory cache with a configurable TTL (default: 5 minutes) for ticker info and history query responses.
3. Safe Log Handling Stdout (sys.stdout) is strictly reserved for the MCP JSON-RPC protocol transport. Standard logs are redirected entirely to stderr (sys.stderr) using a custom structured logger to prevent channel corruption.
4. Crash-Resilient Tool Handlers Every tool is wrapped in strict try-except blocks. Instead of crashing the server, exceptions (e.g., TickerNotFound, network timeouts) are caught, logged, and returned as structured JSON error responses.

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Features & Available MCP Tools

The server exposes 5 standardized tools:

Tool Name	Parameters	Description	Returns
search_company	query: str	Search for matching company names, tickers, and exchanges	JSON array of results
get_stock_price	symbol: str	Retrieve real-time pricing and basic exchange details	Current price, state, currency, time
get_stock_info	symbol: str	Retrieve key fundamentals, stats, and company info	Market cap, P/E, beta, dividend yield, etc.
compare_companies	symbol1: str, symbol2: str	Side-by-side metric comparison of two tickers	Structured comparative matrix
get_stock_history	symbol: str, period: str	Download historical prices (1d, 5d, 1mo, 3mo, 6mo, 1y, 5y, max)	Chronological price records

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Installation

Prerequisites

• Python 3.12 or newer
• Virtual Environment or uv package manager

Standard Setup

1. Clone the repository:

   git clone https://github.com/yourusername/finance-intelligence-mcp.git
   cd finance-intelligence-mcp
   

2. Create a virtual environment and install dependencies:

   python3 -m venv .venv
   source .venv/bin/activate
   pip install --upgrade pip
   pip install -e ".[dev]"
   

3. Configure the environment: Create a .env file from the example:

   cp .env.example .env
   

   (Adjust values like CACHE_TTL_SECONDS or LOG_LEVEL if needed.)

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Usage

Running Locally

To run the server in the default stdio mode (which local clients like Claude Desktop use):

python -m src.main

To run the server in SSE (HTTP) mode:

python -m src.main --transport sse --port 8000

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Client Integration Configurations

1. Claude Desktop Configuration

Add the following to your Claude Desktop configuration file (typically at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

Standard Execution (Python Venv):

{
  "mcpServers": {
    "finance-intelligence": {
      "command": "/Users/YOUR_USER/Desktop/finance mcp/.venv/bin/python",
      "args": [
        "-m",
        "src.main"
      ],
      "env": {
        "LOG_LEVEL": "INFO",
        "CACHE_TTL_SECONDS": "300"
      }
    }
  }
}

Docker Execution:

{
  "mcpServers": {
    "finance-intelligence-docker": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "finance-intelligence-mcp:latest"
      ]
    }
  }
}

2. Cursor Configuration

To add the server to Cursor:

1. Open Cursor Settings -> Features -> MCP.
2. Click + Add New MCP Server.
3. Fill out the fields:
   • Name: Finance Intelligence
   • Type: command
   • Command: /path/to/project/.venv/bin/python -m src.main

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Docker Containerization

1. Build the image:

   docker build -t finance-intelligence-mcp:latest .
   

2. Run the image locally in stdio mode:

   docker run -i --rm finance-intelligence-mcp:latest
   

3. Run the image locally in HTTP/SSE mode:

   docker run -d -p 8000:8000 --name finance-mcp finance-intelligence-mcp:latest --transport sse --port 8000
   

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Example Prompts

Here are some prompt examples you can ask your AI model once the server is connected:

• Search: "Find the stock ticker for Nvidia." -> Triggers search_company(query="Nvidia")
• Price: "What is the current stock price and market state of Apple (AAPL)?" -> Triggers get_stock_price(symbol="AAPL")
• Info: "Show me the key financials for Microsoft, including market cap, beta, and P/E ratio." -> Triggers get_stock_info(symbol="MSFT")
• Comparison: "Compare Apple and Microsoft stocks side by side." -> Triggers compare_companies(symbol1="AAPL", symbol2="MSFT")
• History: "Analyze the historical performance of Tesla (TSLA) over the past month." -> Triggers get_stock_history(symbol="TSLA", period="1mo")

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Quality & Testing

We enforce code quality through automated checks.

Run Tests

pytest tests/ -v

Run Linter

ruff check src/ tests/

Run Type Checker

mypy src/

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Roadmap

Roadmap

• [x] Stock prices
• [x] Company information
• [x] Stock comparison
• [ ] Financial statements
• [ ] Crypto support
• [ ] Macroeconomic indicators
• [ ] Portfolio analysis
• [ ] DCF valuation

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Contributing

Contributions are welcome! Please open an issue or submit a pull request with any suggestions or improvements. Make sure to run the formatting and test suite before submitting code.

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License

This project is licensed under the MIT License - see the LICENSE file for details.