FMP

Provides tools, resources, and prompts to access Financial Modelling Prep data for financial analysis via MCP.

python

0

GitHub Stars

python

Language

4 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

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

You run an MCP server that leverages the Financial Modelling Prep API to deliver financial data tools, resources, and prompts for analysis. This server lets you query company details, stock quotes, financial statements, metrics, ratios, DCF valuations, and more through a programmable, client-friendly interface.

How to use

You connect to the FMP MCP Server from an MCP client, then use its tools to fetch company data, analyze financials, and generate investment insights. Start the server locally, then configure your client to point at the MCP endpoint. Use the provided prompts to drive end-to-end financial analysis workflows, investment research, and sector analysis.

How to install

Prerequisites: you need Python installed to run the MCP server and a command-line tool named uv used to start the server.

Step by step commands to get you running:

# 1. Install dependencies (if applicable in your environment)
# (This project uses uv exercise to run the Python module directly)

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

# 3. Start the server locally
uv run python -m fmp_mcp_server.server

# Optional: use the installed script if available
uv run fmp-mcp-server

Additional notes

API access requires an API key. Place your key in the environment file as FMP_API_KEY. The server supports real-time data access and comprehensive financial analysis prompts.

If you prefer containerized deployment, you can build and run a Docker image using the Docker commands outlined in the deployment section. You can also run tests and format code during development if you contribute to the project.

Available tools

get_company_profile

Fetches comprehensive information about a company, including name, sector, industry, and profile details.

get_stock_quote

Retrieves real-time stock quotes and market data for specified symbols.

get_financial_statements

Returns income statements, balance sheets, and cash flow data for a company.

get_key_metrics

Provides key financial metrics and KPIs for performance analysis.

get_financial_ratios

Offers a collection of financial ratios for evaluating profitability, efficiency, and liquidity.

get_dcf_valuation

Performs discounted cash flow valuation to estimate intrinsic value.

search_companies

Searches for companies by name or symbol to locate relevant data.

get_sector_performance

Provides an overview of market sector performance.

financial_analysis

Prompts and workflow utilities to perform comprehensive financial analysis.

investment_research

Generates detailed investment research reports based on data retrieved from the API.

sector_analysis

Produces sector performance analyses and comparative insights.