fmp-mcp 0.4.0

MCP server for Financial Modeling Prep - 19 tools for market data via Claude

fmp-mcp

Financial intelligence for your AI agent — powered by live market data.

Give Claude (or any MCP-compatible AI) the ability to analyze stocks, screen markets, compare peers, read earnings calls, and track sector rotations — all grounded in real financial data from Financial Modeling Prep.

This isn't a raw API wrapper. Each tool is purpose-built for a specific analytical task, composing multiple data sources into structured, analysis-ready outputs designed for AI consumption.

Tool surface below was verified against fmp/server.py on 2026-04-30. The repo build exposes 20 MCP tools; the two estimate-revision tools require the estimates extra plus database access for useful live results.

What your AI can do

Market Intelligence

• get_market_context — One-call market snapshot: indices, sectors, gainers/losers, economic events
• get_sector_overview — Sector and industry performance with P/E valuations
• get_news — Stock-specific or broad market news
• get_events_calendar — Earnings, dividends, splits, and IPO calendars
• get_economic_data — Economic indicators and high-impact event tracking

Fundamental Analysis

• fmp_fetch — Direct access to 60+ financial data endpoints (income statements, balance sheets, cash flows, key metrics, and more)
• fmp_market_cap_check — Compare current market cap against the latest annual filing value
• compare_peers — Side-by-side peer comparison across 12 financial ratios
• get_earnings_transcript — Parsed earnings calls with speaker attribution and Q&A sections

Stock Screening & Discovery

• screen_stocks — Screen by sector, market cap, beta, dividend, volume, and more
• get_institutional_ownership — Institutional holder analytics and ownership trends
• get_insider_trades — Insider transaction flow and statistics
• get_etf_holdings — ETF/fund holdings, sector and country allocation

Technical Analysis

• get_technical_analysis — Composite signals from 7 indicators (SMA, EMA, RSI, MACD, Bollinger, ADX, Williams %R) with buy/sell scoring

Analyst Sentiment

• get_estimate_revisions — Historical EPS/revenue estimate revision trends
• screen_estimate_revisions — Screen for estimate momentum across a universe of stocks

Data Discovery

• fmp_list_endpoints — Browse all 60+ available data endpoints by category
• fmp_describe — Get parameter documentation for any endpoint
• fmp_search — Search for companies by name or ticker
• fmp_profile — Company profile with sector, industry, and key stats

Install

pip install fmp-mcp

Optional estimate-revision tools (requires PostgreSQL):

pip install "fmp-mcp[estimates]"

Configuration

Set your API key:

export FMP_API_KEY="your_key"

Optional settings:

• FMP_CACHE_DIR — Custom cache directory (default: ~/.cache/fmp-mcp/)
• FMP_CACHE_MAXSIZE — Max in-memory cache entries (default: 200)

Run

fmp-mcp

Or register it with Claude Code from the repo root:

claude mcp add fmp-mcp --scope user \
  -- python3 -m fmp.server

You can also use a generic MCP config:

{
  "mcpServers": {
    "fmp-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["fmp-mcp"],
      "env": { "FMP_API_KEY": "your_key" }
    }
  }
}

How it's different

	Raw API wrapper	fmp-mcp
Approach	Expose every endpoint 1:1	Purpose-built analytical tools
Output	Raw JSON, dozens of fields	Structured, summarized, analysis-ready
Composition	One API call per tool	Multiple sources stitched together
AI-optimized	Generic descriptions	Tool descriptions and schemas designed for LLM tool selection
Caching	None	Per-endpoint disk caching with configurable refresh strategies

Requirements

• Python 3.11+
• FMP API key (get one here)

Related Docs

• docs/reference/FMP_ENDPOINTS.md — registered endpoint catalog
• docs/reference/MCP_SERVERS.md — server registration and troubleshooting