FMP MCP Server Overview

The FMP MCP Server bridges the gap between natural‑language AI assistants and the rich financial data offered by Financial Modeling Prep (FMP). By exposing FMP’s APIs through the Model Context Protocol, it lets investment bankers, M&A analysts and finance professionals perform in‑depth company analysis without leaving their conversational workflow. Instead of manually querying spreadsheets or browsing multiple web pages, a user can ask Claude Desktop to pull the latest financial statements, calculate valuation multiples, or run M&A scenarios—all in a single, context‑aware request.

At its core, the server implements a suite of focused tools that translate common financial queries into precise API calls. These include retrieving company profiles, income statements, balance sheets, cash flow statements, key ratios, enterprise value calculations, and performing company searches by name. Each tool is designed to return structured JSON that Claude can immediately incorporate into its response, ensuring data consistency and reducing the risk of misinterpretation. The server also supports advanced M&A analysis such as comparable company valuation, EV/EBITDA comparisons, and post‑acquisition financial modeling.

For developers building AI‑augmented finance workflows, the server offers several practical advantages. It eliminates the need to write custom API wrappers or handle authentication logic, as the MCP handles request routing and parameter validation. The integration is plug‑and‑play: once the server is added to a Claude Desktop configuration, any prompt that matches one of the tool signatures triggers an automatic data fetch. This seamless interaction means analysts can iterate quickly—ask for a company’s P/E ratio, then immediately request its cash flow statements to assess liquidity—all within the same conversational thread.

Real‑world scenarios where this server shines include:

• Deal Sourcing – Identify mid‑cap acquisition targets by filtering on EBITDA margins and debt levels.
• Valuation Workflows – Compare EV/EBITDA multiples across a sector to spot over‑ or under‑priced peers.
• Post‑Acquisition Modeling – Estimate pro‑forma debt/EBITDA ratios for hypothetical buyouts.
• Strategic Fit Analysis – Evaluate whether a potential acquisition makes sense based on sector overlap, size compatibility, and financial strength.

Because the MCP server adheres to a standardized protocol, it can be combined with other data sources (e.g., Bloomberg, SEC filings) or extended with custom tools without disrupting existing workflows. Its design prioritizes clarity and speed: the server returns only the fields requested, minimizing latency and keeping Claude’s responses concise. In environments where time is money, this focused approach allows finance professionals to leverage AI for rapid, data‑driven decision making without sacrificing accuracy or depth.