How do I use FMP MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@FMP MCP Server Give me a detailed stock brief for NVDA including valuation and analyst ratings" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

FMP MCP Server

A Model Context Protocol server that provides financial data from Financial Modeling Prep for AI-assisted investment research.

Built with FastMCP 2.0 and Python.

Tools

Workflow Tools (start here)

High-level tools that orchestrate multiple API calls into single research-ready responses:

Tool	Description
`stock_brief`	Quick comprehensive snapshot: profile, price action, valuation, analyst consensus, insider signals, headlines
`market_context`	Full market environment: rates, yield curve, sector rotation, breadth, movers, economic calendar
`earnings_setup`	Pre-earnings positioning: consensus estimates, beat/miss history, analyst momentum, price drift, insider signals
`fair_value_estimate`	Multi-method valuation: DCF, earnings-based, peer multiples, analyst targets, blended estimate
`earnings_postmortem`	Post-earnings synthesis: beat/miss, trend comparison, analyst reaction, market response, guidance tone

Atomic Tools (deeper dives)

Tool	Description
`company_overview`	Company profile, quote, key metrics, and analyst ratings
`financial_statements`	Income statement, balance sheet, cash flow (annual/quarterly)
`analyst_consensus`	Analyst grades, price targets, and forward estimates
`earnings_info`	Historical and upcoming earnings with beat/miss tracking
`price_history`	Historical daily prices with technical context
`stock_search`	Search for stocks by name or ticker
`insider_activity`	Insider trading activity and transaction statistics
`institutional_ownership`	Top institutional holders and position changes
`stock_news`	Recent news and press releases
`treasury_rates`	Current Treasury yields and yield curve
`economic_calendar`	Upcoming economic events and releases
`market_overview`	Sector performance, gainers, losers, most active
`earnings_transcript`	Earnings call transcripts with pagination support
`revenue_segments`	Revenue breakdown by product and geography
`peer_comparison`	Peer group valuation and performance comparison
`dividends_info`	Dividend history, yield, growth, and payout analysis

Setup

Prerequisites

Python 3.11+
uv (recommended) or pip
An FMP API key

Install

uv sync

Configure

Set your API key as an environment variable:

export FMP_API_KEY=your_api_key_here

Or create a .env file:

FMP_API_KEY=your_api_key_here

Run

uv run fastmcp run server.py

Claude Desktop / Claude Code

Add to your MCP config:

{ "mcpServers": { "fmp": { "command": "uv", "args": ["run", "--directory", "/path/to/fmp", "fastmcp", "run", "server.py"], "env": { "FMP_API_KEY": "your_api_key_here" } } } }

Testing

uv run pytest tests/ -v

All tools are tested with mocked API responses using respx.

Architecture

server.py # FastMCP entry point, registers all tool modules fmp_client.py # Async HTTP client with TTL caching and graceful error handling tools/ overview.py # company_overview, stock_search financials.py # financial_statements, revenue_segments valuation.py # analyst_consensus, peer_comparison market.py # price_history, earnings_info, dividends_info ownership.py # insider_activity, institutional_ownership news.py # stock_news macro.py # treasury_rates, economic_calendar, market_overview transcripts.py # earnings_transcript (with pagination) workflows.py # stock_brief, market_context, earnings_setup, fair_value_estimate, earnings_postmortem

Key design decisions:

Module pattern: Each tool file exports register(mcp, client) to keep tools organized
Parallel fetches: Workflow tools use asyncio.gather() to call multiple endpoints concurrently
Graceful degradation: FMPClient.get_safe() returns defaults on error so composite tools return partial data instead of failing entirely
In-memory TTL cache: Avoids redundant API calls with configurable TTLs per data type (60s for quotes, 24h for profiles)