Financial Modeling Prep (FMP) MCP Server

Financial Modeling Prep (FMP) MCP Server

By shadi-fsai GitHub

Financial Modeling Prep MCP Server

Overview

What is Financial Modeling Prep (FMP) MCP Server?

The FMP MCP Server is a Model Context Protocol server that provides access to Financial Modeling Prep (FMP) API data through a standardized interface, allowing AI assistants to programmatically access financial data.

How to use FMP MCP Server?

To use the FMP MCP Server, clone the repository, set up your environment with the required API key, and run the server using either UV or pip. Connect your AI assistant, like Claude, to the server to access financial data.

Key features of FMP MCP Server?

  • Access to company profiles, financial statements, and key metrics.
  • Retrieval of SEC filings and earnings transcripts.
  • Current market data including stock prices and treasury yields.
  • Competitor analysis capabilities.

Use cases of FMP MCP Server?

  1. AI assistants retrieving financial data for analysis.
  2. Investors accessing company financials for decision-making.
  3. Analysts finding SEC filings and earnings transcripts for research.

FAQ from FMP MCP Server?

  • What is required to run the server?

    You need Python 3.8 or higher and a Financial Modeling Prep API key.

  • Can I use this server with AI assistants?

    Yes! The server is designed to work with AI assistants like Claude.

  • Is there a caching system implemented?

    Yes, the server caches financial data to improve performance.

Content

Financial Modeling Prep (FMP) MCP Server

A Model Context Protocol (MCP) server that provides access to Financial Modeling Prep (FMP) API data through a standardized interface. This server allows AI assistants like Claude to access financial data programmatically.

Features

  • Company Profiles: Access company information, descriptions, market caps, employee counts, and industry data
  • Financial Statements: Retrieve income statements, balance sheets, and cash flow statements
  • Financial Metrics: Get key metrics, ratios, and growth data
  • Analyst Data: Access analyst estimates and recommendations
  • SEC Filings: Find and retrieve SEC filing content
  • Earnings Transcripts: Get earnings call transcripts
  • Market Data: Access current stock prices and treasury yields
  • Competitor Analysis: Find competitor companies

Installation

Prerequisites

  • Python 3.8 or higher
  • UV package manager (recommended) or pip
  • Financial Modeling Prep API key

Setup

  1. Clone this repository

  2. Create a .env file in the project root with your API key:

    # Financial Modeling Prep API Configuration
    FMP_KEY=your_api_key_here
    
    # Optional: SEC API Configuration
    SEC_ACCESS=YourCompanyName YourEmail@example.com
    
  3. Install dependencies using UV (recommended):

    uv venv
    uv pip install -r requirements.txt
    

    Or using pip:

    pip install -r requirements.txt
    

Running the Server

UV provides faster dependency resolution and installation. To run the server with UV:

# Activate the virtual environment
uv venv activate

# Run the server
python fmp_mcp_server.py

The server will start and listen for connections on the default MCP port.

Using pip

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Run the server
python fmp_mcp_server.py

Connecting with Claude Desktop

Claude Desktop can connect to MCP servers to access financial data. Here's how to set it up:

  1. Download Claude Desktop
  2. Edit claude_desktop_config.json: "fmp_mcp_server": { "command": "uv", "args": [ "--directory", "REPLACE ME WITH ABSOLUTE DIRECTORY TO REPO", "run", "fmp_mcp_server.py" ] }

Now Claude can use the FMP data through the MCP interface. You can ask Claude to:

  • Get company profiles
  • Retrieve financial statements
  • Find SEC filings
  • Access market data
  • And more!

Example Queries for Claude

Once connected, you can ask Claude questions like:

  • "I am considering a 3 year horizon investment, is Apple a good investment?"
  • "Show me Tesla's latest quarterly income statement"
  • "Find the latest 10-K filing for Microsoft"
  • "What are Amazon's main competitors?"
  • "Get the latest earnings transcript for Meta"

Configuration Options

The server supports the following environment variables:

  • FMP_KEY: Your Financial Modeling Prep API key (required)
  • SEC_ACCESS: Your company name and email for SEC API access (optional)

Caching

The server implements a caching system to reduce API calls and improve performance:

  • Financial data is cached by quarter/year
  • Profile data is cached monthly
  • Daily price data is cached for the current day

Cache files are stored in the DataCache directory.

Logging

Logs are written to the logs directory with rotation enabled:

  • Maximum log file size: 10MB
  • Number of backup files: 5

License

MIT License

Acknowledgements

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