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Financial Modeling Prep (fmpsdk)

Exposes Financial Modeling Prep market, company, statement, screening, and event data through MCP tools.

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Install Financial Modeling Prep (fmpsdk)

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "financial-modeling-prep-fmpsdk": {
      "command": "uvx",
      "args": [
        "fmpsdk-mcp"
      ],
      "env": {
        "FMP_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Automated check passed— started and listed 13 tools correctly (7d ago).

Overview

The Financial Modeling Prep (fmpsdk) MCP server exposes Financial Modeling Prep API data through MCP tools for AI assistants. It provides 10 curated tools for common requests and discovery tools that can reach roughly 240 SDK methods at runtime. Use it for quotes, company data, financial statements, ratios, price history, news, and other supported FMP datasets when you have an FMP API key. The server runs as a Python package through the `fmpsdk-mcp` distribution and can be launched with `uvx`.

Use cases

  • Query current and aftermarket quotes for a symbol
  • Retrieve company profiles, statements, ratios, and valuation data
  • Compare historical prices and technical indicators
  • Search symbols or screen companies
  • Check earnings, dividends, filings, and market news

Key features

  • 10 curated MCP tools for common financial data requests
  • Runtime discovery of roughly 240 SDK methods
  • Quotes, profiles, statements, ratios, prices, and news
  • Support for market, reference, ownership, calendar, and alternative-market data
  • Endpoint-level plan and authentication errors

Tools13

What Financial Modeling Prep (fmpsdk) lets your agent do. Expand a tool for its parameters.

Verified live~1.2k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

8/8 checks healthy over the last 45d

What it can do

From the 13 tools this server returned when we ran it.

Makes network requests · 1Read-only tools · 13
Network at startup2 connections
Files written at startup30

Observed 2026-10-15 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 15d ago via OSV.dev · fmpsdk-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars215Stargazers on the source repository.
Last commit1mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed13Callable tools this server registers over MCP.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Financial Modeling Prep (fmpsdk) works

What Financial Modeling Prep (fmpsdk) MCP server does

The Financial Modeling Prep (fmpsdk) MCP server connects an MCP-compatible assistant to the Financial Modeling Prep API through the fmpsdk Python library. Its interface covers market data, company fundamentals, reference data, screening, calendars, ownership, alternative markets, sentiment, analysis, and bulk downloads.

The MCP package offers 10 curated tools for frequent requests, including quotes, company profiles, financial statements, ratios, historical prices, and news. Discovery tools expose the broader SDK surface at runtime, covering approximately 240 methods across 29 data groups. The underlying library supports both direct method names and category namespaces, although MCP clients interact through the server tools rather than calling the Python client directly.

How it works

An MCP client starts the fmpsdk-mcp process and supplies an FMP API key through the environment. The server sends requests to FMP using the SDK and returns the resulting data to the assistant. Most SDK calls return lists of dictionaries, so an agent may need to interpret or organize the returned records for the requested task.

FMP plan access applies at the endpoint level. The SDK documents the required tier for each method, and calls outside the account’s plan raise an FMPPlanLimitError. Invalid credentials raise an FMPAuthenticationError, making authentication and plan failures distinct from successful data responses.

Setup and configuration

Install or run the separate MCP distribution rather than relying on the base SDK package to install MCP dependencies. With uv, the documented launch command is:

Terminal
claude mcp add fmpsdk -s user -e FMP_API_KEY=your-key -- uvx fmpsdk-mcp

Set FMP_API_KEY to an active Financial Modeling Prep key. The repository also provides Claude Desktop configuration details in the MCP-specific README. The underlying Python package requires Python 3.9 or newer; python-dotenv is optional for loading a key from a .env file when using the library directly.

Tools and capabilities

The Financial Modeling Prep (fmpsdk) MCP server can support requests involving:

  • Real-time and aftermarket quotes, historical prices, and technical indicators
  • Company profiles, executives, valuation, financial statements, ratios, and growth data
  • Symbol, CIK, CUSIP, and ISIN lookup plus company screening
  • Earnings, dividends, splits, IPO, and economic calendars
  • Insider transactions, institutional Form 13F data, SEC filings, and ESG disclosures
  • Crypto, foreign exchange, commodities, indexes, ETFs, and mutual funds
  • Analyst information, market movers, congressional trading, and other analysis data
  • Bulk downloads and runtime discovery of the wider SDK method set

Limitations and notes

Coverage depends on the Financial Modeling Prep plan associated with the API key. Free, Starter, Premium, and Ultimate plans expose different endpoint sets, and a plan-restricted request will fail instead of returning partial data.

The seven TipRanks methods are a special case. They require a separate paid TipRanks data add-on, including for Ultimate accounts, and the repository says they have only been tested with mocks rather than verified against live responses. Treat those methods as unverified.

The repository’s generated OpenAPI file describes the SDK-derived API surface; it is generated from the library source and is not an OpenAPI document published or endorsed by FMP. Older SDK releases that target the retired legacy API should not be used for current requests.

Read the full README View source on GitHub

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Frequently Asked Questions about Financial Modeling Prep (fmpsdk)

Financial Modeling Prep (fmpsdk) is an MCP server that connects AI assistants to the Financial Modeling Prep API through the fmpsdk Python library. Its main tools cover quotes, company profiles, financial statements, ratios, historical prices, news, and runtime discovery of roughly 240 additional methods.

Run it with `uvx fmpsdk-mcp` and provide `FMP_API_KEY`; the documented Claude command is `claude mcp add fmpsdk -s user -e FMP_API_KEY=your-key -- uvx fmpsdk-mcp`. The MCP distribution is separate from the base `fmpsdk` package.

It requires `FMP_API_KEY`. Set that variable to an active Financial Modeling Prep API key before starting the MCP process.

It can access quotes, profiles, statements, ratios, historical prices, news, screening, calendars, ownership, alternative markets, analysis data, and other SDK methods exposed through runtime discovery. Availability also depends on the FMP plan.

Yes, the repository documents Claude Desktop configuration for the MCP server. It also names Claude Code as a supported usage target and provides a `uvx` launch command for Claude.

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