How to Build an MCP Server with FastMCP for Your Local AI Agent

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How to Build an MCP Server with FastMCP for Your Local AI Agent

Tópico:
How to Build an MCP Server with FastMCP for Your Local AI Agent

Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

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In this tutorial, I'll show you how to build an MCP server with FastMCP, connect your local AI agent to use tools from the local MCP server that you built, and add support for remote MCP servers. We'll wire the whole thing together with LangChain v1, Ollama, Qwen, and Python.

Model Context Protocol (MCP) is the common language between AI agents and tools. It's the standard way to expose tools to AI agents.

More companies are starting to expose MCP servers alongside their existing APIs, because MCP gives LLMs and AI agents a standard way to discover and use those capabilities directly.

Table of Contents

• Background

• What is MCP?

• What is FastMCP?

• Motivation and Architecture

• Step 1: Install Ollama and Pull the Model

• Step 2: Install Python Dependencies

• Step 3: Build the Local MCP Server with FastMCP

• Step 4: Agent Python Code

• Step 5: Run the Agent

• Conclusion

Background

A lot of simple local AI agents define their tools directly inside the same Python script as the agent. These are specific to the agent and every new agent has to re-implement the same tools from scratch.

MCP improves this by giving tools a standard interface that any MCP-compatible client can use. Write the tool once as an MCP server, and any compatible client can reuse it. And because MCP is a network protocol, those tools don't even have to run on your machine. Someone else can host an MCP server, and your agent can use its tools the same way it uses your local ones.

To follow this tutorial, you'll need Ollama installed on your machine. The tutorial works on macOS, Windows, and Linux. I'm using a MacBook Pro with 32 GB of RAM, but you can run this on a lower-memory machine by choosing a smaller Qwen model from Ollama.

What is MCP?

MCP (Model Context Protocol) is an open protocol that exposes tools, resources, and prompts to LLM clients.

Just as REST standardized many web APIs, MCP is the standardizing protocol for AI tools. Instead of every framework inventing its own tool interface, MCP defines a shared one, and anything that understands the protocol can use tools exposed by any MCP-compatible server.

The below image from modelcontextprotocol.io captures the idea well.

An MCP server is a small program that exposes a list of tools. An MCP client is anything that connects to that server (for example, an AI agent) and lets an LLM call those tools.

MCP servers are commonly exposed over transports like:

• stdio: the server runs as a subprocess of the client, communicating over stdin/stdout. Best for local tools that only your agent needs.

• http: the server runs as an HTTP service and clients connect over the network. Best for shared or remote tools.

The protocol standardizes how tools are exposed so different AI agents and clients can use them consistently.

What is FastMCP?

FastMCP is a Python library that makes writing an MCP server feel like writing a FastAPI app. You decorate functions with

Código Selecionar

@mcp.tool, and FastMCP handles the protocol details: JSON-RPC messages, tool schema generation from your type hints and docstrings, and the transport layer.

On the LangChain side,

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langchain-mcp-adaptersis a library that connects to one or more MCP servers and loads their tools into a format LangChain v1's

Código Selecionar

create_agentcan use directly. The agent code doesn't know if a tool lives in a subprocess on your

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