FMP MCP Server

A Model Context Protocol (MCP) server that provides tools, resources, and prompts for financial analysis using the Financial Modelling Prep API.

Features

Tools

• get_company_profile: Get comprehensive company information
• get_stock_quote: Real-time stock quotes and market data
• get_financial_statements: Income statement, balance sheet, and cash flow data
• get_key_metrics: Key financial metrics and KPIs
• get_financial_ratios: Comprehensive financial ratios for analysis
• get_dcf_valuation: Discounted cash flow valuation
• search_companies: Search for companies by name or symbol
• get_sector_performance: Market sector performance overview

Resources

• Market Sectors: Real-time sector performance data
• Company Profiles: Detailed company information
• Financial Statements: Complete financial statement data

Prompts

• financial_analysis: Comprehensive financial analysis workflow
• investment_research: Detailed investment research report
• sector_analysis: Sector performance and comparison analysis

Setup

1. Install dependencies:

uv sync

2. Configure API access:

cp .env.example .env
   # Edit .env and add your Financial Modelling Prep API key

3. Get API Key:

Usage

With Claude Code

Add to your Claude Code MCP configuration:

{
  "mcpServers": {
    "fmp": {
      "command": "uv",
      "args": ["run", "python", "-m", "fmp_mcp_server.server"],
      "env": {
        "FMP_API_KEY": "your_api_key_here"
      }
    }
  }
}

Direct Usage

# Run the server
uv run python -m fmp_mcp_server.server

# Or use the installed script
uv run fmp-mcp-server

Docker Usage

Build and run with Docker

# Build the image
docker build -t fmp-mcp-server .

# Run with environment file
docker run --env-file .env fmp-mcp-server

Using Docker Compose

# Start the service
docker-compose up -d

# View logs
docker-compose logs -f

# Stop the service
docker-compose down

Using pre-built image from GitHub Container Registry

docker run --env-file .env ghcr.io/ccdatatraits/fmp-mcp-server:latest

Development

1. Install with development dependencies:

uv sync --dev

2. Run tests:

uv run pytest

3. Format code:

uv run black src/
   uv run ruff check src/

4. Type checking:

uv run mypy src/

API Rate Limits

The Financial Modelling Prep API has rate limits depending on your subscription:

• Free: 250 requests/day
• Starter: 300 requests/minute
• Professional: 2000 requests/minute

Configure rate limiting in your `.env` file if needed.

License

MIT License