iflow-mcp_barvhaim-yfinance-mcp-server 0.1.0

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YFinance MCP Server

A comprehensive Model Context Protocol (MCP) server that provides financial data through Yahoo Finance API integration. This server enables AI agents to access real-time stock market data, historical prices, financial statements, and market analysis.

Features

• 10 Comprehensive Financial Tools for complete market data access
• Real-time Stock Information including prices, market cap, and key metrics
• Historical Data Analysis with flexible time periods and intervals
• Financial Statements (income statement, balance sheet, cash flow)
• Earnings Data (annual and quarterly)
• Dividend and Split History
• News and Analyst Recommendations
• Stock Search and Multi-quote Support
• Robust Error Handling with structured JSON responses
• FastMCP Framework with async support for high performance

Quick Start

Prerequisites

• Python 3.11+
• uv (Python package manager)

Installation

# Clone the repository
git clone https://github.com/barvhaim/yfinance-mcp-server.git
cd yfinance-mcp-server

# Install dependencies
uv sync

Running the Server

# Start the MCP server
uv run main.py

# The server will start and be ready to accept MCP client connections

Available Tools

1. get_stock_info

Get comprehensive stock information including current price, market cap, and financial metrics.

Parameters:

• symbol (str): Stock ticker symbol (e.g., 'AAPL', 'GOOGL')

Returns: Stock information including price, market cap, P/E ratio, dividend yield, 52-week range, volume, beta, and company details.

2. get_historical_data

Retrieve historical stock price data with flexible time periods and intervals.

Parameters:

• symbol (str): Stock ticker symbol
• period (str): Time period (1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max)
• interval (str): Data interval (1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo)

Returns: Historical OHLCV data with dates and volume information.

3. get_dividends

Get dividend payment history for a stock.

Parameters:

• symbol (str): Stock ticker symbol

Returns: List of dividend payments with dates and amounts.

4. get_splits

Retrieve stock split history.

Parameters:

• symbol (str): Stock ticker symbol

Returns: List of stock splits with dates and split ratios.

5. get_financials

Get comprehensive financial statements.

Parameters:

• symbol (str): Stock ticker symbol
• quarterly (bool): Get quarterly data if True, annual if False

Returns: Income statement, balance sheet, and cash flow statement data.

6. get_earnings

Retrieve earnings data for analysis.

Parameters:

• symbol (str): Stock ticker symbol

Returns: Annual and quarterly earnings data.

7. get_news

Get recent news articles related to a stock.

Parameters:

• symbol (str): Stock ticker symbol
• count (int): Number of articles to return (default: 10)

Returns: List of news articles with titles, links, publishers, and timestamps.

8. get_recommendations

Get analyst recommendations and ratings.

Parameters:

• symbol (str): Stock ticker symbol

Returns: List of analyst recommendations with firms, ratings, and actions.

9. search_stocks

Search for stocks by company name or ticker symbol.

Parameters:

• query (str): Search query (company name or ticker)
• limit (int): Maximum results to return (default: 10)

Returns: List of matching stocks with symbols, names, and exchange information.

10. get_multiple_quotes

Get current quotes for multiple stocks simultaneously.

Parameters:

• symbols (List[str]): List of stock ticker symbols

Returns: Dictionary of stock quotes with current prices, changes, and basic metrics.

Usage Examples

Basic Stock Information

# Get Apple stock information
result = await get_stock_info("AAPL")
print(f"Current Price: ${result['current_price']}")
print(f"Market Cap: ${result['market_cap']:,}")

Historical Data Analysis

# Get 1-year daily data for Google
result = await get_historical_data("GOOGL", period="1y", interval="1d")
print(f"Retrieved {result['count']} data points")

Multiple Stock Quotes

# Get quotes for tech stocks
result = await get_multiple_quotes(["AAPL", "GOOGL", "MSFT", "AMZN"])
for symbol, quote in result['quotes'].items():
    print(f"{symbol}: ${quote['current_price']}")

MCP Client Integration

Claude Desktop Integration

To connect this server with Claude Desktop:

1. Start the server in one terminal:

   uv run main.py
   

2. Configure Claude Desktop by editing your MCP settings file:

   macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

   Windows: %APPDATA%\Claude\claude_desktop_config.json

3. Add the server configuration:

   {
      "yfinance": {
        "command": "uv",
        "args": [
          "--directory",
          "/path/to/yfinance-mcp-server",
          "run",
          "main.py"
        ]
      }
    }
   

4. Update the cwd path to your actual project directory

5. Restart Claude Desktop to load the new server

6. Verify connection by asking Claude: "What financial data tools do you have available?"

Alternative: Direct Connection

For other MCP clients:

1. Start the server: uv run main.py
2. Configure your MCP client to connect to the server endpoint
3. Tools will be automatically discovered by your AI agent
4. Use standard stock symbols (AAPL, GOOGL, MSFT, etc.) with the tools

Development

Code Formatting

# Format code with black
uv run black .

# Check formatting
uv run black --check .

# Show formatting differences
uv run black --diff .

Adding New Tools

1. Create a new async function in main.py
2. Decorate with @mcp.tool()
3. Add proper type hints and docstrings
4. Include error handling
5. Test the implementation

Environment Variables

Copy .env.sample to .env for any configuration needed:

cp .env.sample .env

Error Handling

All tools include comprehensive error handling:

• Invalid symbols return structured error messages
• Network issues are caught and reported
• Data unavailability is handled gracefully
• Rate limiting is respected automatically by yfinance

Performance Considerations

• Async operations for optimal performance
• Efficient data serialization with structured JSON
• Minimal data processing to reduce latency
• Built-in caching by yfinance for frequently accessed data

Dependencies

• fastmcp - FastMCP framework for MCP server implementation
• yfinance - Yahoo Finance API for financial data
• python-dotenv - Environment variable management
• black - Code formatting
• pydantic - Data validation and serialization

License

This project is available under the MIT License.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Format code with black
6. Submit a pull request

Support

For issues and questions:

• Check the Issues section
• Review the CLAUDE.md file for development guidance
• Ensure all dependencies are properly installed with uv sync

Changelog

v0.1.0

• Initial implementation with 10 financial data tools
• FastMCP server framework integration
• Comprehensive error handling
• Full yfinance API coverage
• MCP client integration support