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dino65-dev

Finance MCP Server

by dino65-dev

Finance_mcp-server

A Model Context Protocol (MCP) server that provides real-time financial data to Large Language Models through Yahoo Finance.

This project creates an MCP server that allows AI models like Claude to access real-time stock and financial data through the Yahoo Finance API. The server implements the Model Context Protocol standard, enabling seamless integration with various MCP clients including Claude Desktop, Cursor, Winds AI, and others.

🚀 Features

  • Real-time Stock Price Lookup: Get current prices for any publicly traded company

  • Historical Data Analysis: Retrieve stock performance over custom time periods

  • Company Information: Access detailed company profiles and financial metrics

  • Stock Comparison: Compare multiple stocks based on various metrics

  • Stock Search: Find relevant stocks by company name or keywords

  • Resource Access: Use structured URI schemes for financial data access

Related MCP server: MCP Yahoo Finance

📋 Requirements

💻 Installation

  1. Clone this repository:

    git clone https://github.com/dino65-dev/Finance_mcp-server.git
  2. Create a virtual environment:

    python -m venv venv
    On Windows: venv\Scripts\activate

Create a virtual environment (recommended) for faster creation:

pip install uv
uv venv
On Windows: venv\Scripts\activate
  1. Install dependencies:

    pip install -r requirements.txt

🔧 Usage

Running the Server

Start the server by running:

python yfinance_mcp_server.py

The server will run as a stdin/stdout process that communicates via the MCP protocol.

Integrating with MCP Clients

Claude Desktop

  1. Open Claude Desktop

  2. Go to Settings

  3. Add an MCP configuration with:

    {
      "mcpServers": {
        "yfinance": {
          "command": "python",
          "args": [
            "/absolute/path/to/yfinance_mcp_server.py"
          ]
        }
      }
    }
  4. Save and restart Claude Desktop

Cursor

  1. Open Cursor and access settings

  2. Navigate to MCP section

  3. Add a new global MCP server with the configuration:

    {
      "yfinance": {
        "command": "python",
        "args": [
          "/absolute/path/to/yfinance_mcp_server.py"
        ]
      }
    }
  4. Start a new chat to use the financial tools

📊 Available Tools

The server provides the following tools:

  1. get_stock_price: Get current stock prices

    Example: Get the current price of Apple stock
  2. get_historical_data: Retrieve historical price data

    Example: Get the stock history for TSLA over the past 3 months
  3. get_stock_metric: Access specific financial metrics

    Example: What is Amazon's market capitalization?
  4. compare_stocks: Compare multiple stocks by metrics

    Example: Compare the P/E ratios of Google, Microsoft, and Apple
  5. search_stocks: Find stocks by name or keyword

    Example: Find stocks related to artificial intelligence

🔍 Resource URIs

Access stock information directly through resource URIs:

  • finance://SYMBOL/info - Get basic information about a stock

📄 License

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

🙏 Acknowledgements

📞 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Related MCP Connectors

  • Deterministic profitability and market-value analysis tools for AI agents — margins, ROA, ROE, ROCE, ROIC, EPS, P/E, P/B, dividend yield and payout ratio via Model Context Protocol. Useful for corporate finance, equity analysis, financial analysis, quantitative analysis, financial formulas and financial modeling.

  • Deterministic company valuation and corporate finance tools for AI agents — IRR, NPV, MOIC, DCF, WACC, enterprise value, EV multiples, CAPM, beta and sensitivity analysis via Model Context Protocol. Useful for financial analysis, equity analysis, quantitative analysis, financial projections, financial formulas and financial modeling.

  • A Model Context Protocol server exposing real-time and historical Colombo Stock Exchange (CSE) data to AI agents and LLM applications. Provides quotes and OHLCV price history, full financial statements (income, balance sheet, cash flow), pre-computed technicals (moving averages, RS ratings, volume signals), macroeconomic indicators, corporate actions, and rule-based screening across CSE stocks and sector indices, everything needed to build CSE-aware trading assistants, research tools, and market-analysis agents. This is the official MCP server of www.ceyloncharts.com

  • Deterministic liquidity and leverage ratio tools for AI agents — current, quick and cash ratios, defensive interval, debt-to-equity, debt-to-assets, equity multiplier and interest coverage via Model Context Protocol. Useful for corporate finance, credit analysis, financial analysis, financial formulas and financial modeling.

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