FastAPI MCP – A Minimal, Plug‑In Model Context Protocol Server

FastAPI MCP is a lightweight example that demonstrates how to expose the Model Context Protocol (MCP) on an existing FastAPI application. By mounting an MCP sub‑application at the root URL (), developers can turn any FastAPI service into a ready‑to‑use AI assistant backend that understands MCP’s resource, tool, prompt, and sampling concepts. The server is intentionally minimal: it contains only the core MCP logic () and a simple FastAPI wrapper (), making it easy to copy into larger projects or to use as a learning reference.

Why MCP and FastAPI Matter

AI assistants like Claude rely on the MCP to discover and invoke external capabilities. Without an MCP server, an assistant would have no standardized way to query a tool’s inputs, outputs, or usage limits. FastAPI, on the other hand, is a modern, asynchronous web framework that already enjoys widespread adoption in Python microservices. By combining the two, FastAPI MCP provides a ready‑to‑go bridge between an assistant and any Python codebase, enabling developers to expose complex business logic as MCP tools with minimal friction.

Core Features and Value

• Root‑mount simplicity – The MCP server is mounted at the root path, ensuring that all MCP endpoints () are directly reachable without additional routing configuration.
• Resource, tool, prompt, and sampling support – The server implements the full MCP contract, allowing assistants to list available resources, call tools with structured arguments, fetch prompts, and sample from model outputs.
• FastAPI integration – By leveraging FastAPI’s dependency injection, background tasks, and async handling, the server can scale with the rest of the application’s ecosystem.
• Client demo – A companion client script shows how to query and invoke MCP endpoints using the official MCP Python SDK, giving developers a quick reference for building their own integrations.

Real‑World Use Cases

• Enterprise chatbot platforms – Embed business logic (e.g., ticketing, CRM queries) as MCP tools so that a conversational AI can interact with internal systems transparently.
• Data‑centric assistants – Expose data processing pipelines or analytical functions as tools that the assistant can call with specific parameters, returning results directly into the conversation.
• Testing and prototyping – Quickly spin up an MCP server during development to validate tool contracts or experiment with new prompt templates without deploying a full application.

Integration into AI Workflows

Once the MCP server is running, an AI assistant simply needs to be configured with its URL. The assistant will then discover the server’s capabilities via standard MCP discovery calls (, etc.). Because the server is built on FastAPI, it can coexist with other REST endpoints or GraphQL services in the same application, allowing developers to maintain a single deployment while offering both traditional APIs and MCP‑enabled AI interactions.

Unique Advantages

• Zero configuration for the MCP core – The example ships with a fully functional MCP implementation that requires no additional setup beyond mounting the sub‑application.
• Docker friendly – A Dockerfile is provided, enabling quick containerization and deployment in cloud environments.
• Extensibility – The modular design lets developers replace or extend the MCP logic () without touching the FastAPI wrapper, keeping concerns separated.

In summary, FastAPI MCP offers a clean, minimal starting point for developers who want to expose their Python services as AI‑ready tools. By marrying FastAPI’s robustness with MCP’s standardized protocol, it lowers the barrier to integrating sophisticated AI assistants into existing workflows.