Financial Modeling Prep MCP Server

The Financial Modeling Prep (FMP) MCP Server bridges the gap between AI assistants and real‑time financial data. By exposing a rich set of tools, resources, and prompts that mirror the FMP API, it allows developers to embed authoritative market intelligence directly into conversational agents. Instead of hard‑coding data pulls or building custom parsers, a Claude or other AI assistant can issue high‑level requests—such as “give me the latest earnings of Apple” or “compare the P/E ratios of the S&P 500 constituents”—and receive structured, ready‑to‑use responses.

What Problem Does It Solve?

Financial analysis often requires pulling data from multiple endpoints: company profiles, statements, market snapshots, and even technical indicators. Traditionally this demands a bespoke integration layer for each data source. The FMP MCP Server consolidates all these capabilities into a single, well‑documented MCP endpoint. Developers can therefore treat financial data as first‑class “tools” in the same way they would call a weather API or a unit‑conversion service, dramatically reducing boilerplate code and the risk of version drift.

Core Value for AI‑Powered Development

• Declarative Data Access – Clients specify what they need (e.g., a balance sheet, an EMA value) and the server returns the precise JSON payload.
• Consistency Across Asset Classes – A unified quote endpoint standardizes how stocks, forex, crypto, commodities, and indices are queried, simplifying client logic.
• Built‑in Prompt Templates – Predefined analysis prompts let assistants generate investment reports or trade ideas without custom prompt engineering.
• Transport Flexibility – Support for stdio, SSE, and streamable HTTP ensures the server can be used in command‑line tools, web services, or real‑time dashboards.
• Stateful & Stateless Modes – Developers can choose a lightweight stateless deployment for quick queries or a stateful setup that remembers context across interactions.

Key Features & Capabilities

Real‑World Use Cases

• Investment Research Bots – An AI assistant can pull the latest earnings, compute valuation ratios, and generate a concise report for portfolio managers.
• Trading Strategy Prototyping – Developers can request EMA values or historical price series, feed them into a reinforcement learning model, and observe live feedback.
• Financial Education Platforms – Interactive chat agents can walk users through the components of a balance sheet or explain why a particular index is trending upward.
• Compliance & Risk Dashboards – Automated checks against market hours and holiday schedules help ensure trades are executed only during valid windows.

Integration into AI Workflows

Because the server adheres to MCP conventions, any compliant assistant can treat it like a standard tool:

1. Tool Invocation – The client sends an MCP request specifying the desired endpoint (e.g., ).
2. Response Handling – The assistant receives a structured payload and can immediately embed it in the conversation or pass it to downstream modules.
3. Prompt Templates – Built‑in prompts can be invoked with minimal parameters, allowing the assistant to generate narrative analyses without manual prompt crafting.

This seamless flow eliminates the need for custom adapters, enabling rapid iteration on financial applications powered by AI.