docs/tutorials/rest-api.mdx

---
title: "How to Connect an LLM to a REST API"
sidebarTitle: "Connect LLMs to REST APIs"
description: "A step-by-step guide to making any REST API with an OpenAPI spec available to LLMs using FastMCP."
icon: "plug"
---

You've built a powerful REST API, and now you want your LLM to be able to use it. Manually writing a wrapper function for every single endpoint is tedious, error-prone, and hard to maintain.

This is where **FastMCP** shines. If your API has an OpenAPI (or Swagger) specification, FastMCP can automatically convert your entire API into a fully-featured MCP server, making every endpoint available as a secure, typed tool for your AI model.

This guide will walk you through converting a public REST API into an MCP server in just a few lines of code.

<Tip>
Every code block in this tutorial is a complete, runnable example. You can copy and paste it into a file and run it, or paste it directly into a Python REPL like IPython to try it out.
</Tip>

### Prerequisites

Make sure you have FastMCP installed. If not, follow the [installation guide](/getting-started/installation).

```bash
pip install fastmcp
```

## Step 1: Choose a Target API

For this tutorial, we'll use the [JSONPlaceholder API](https://jsonplaceholder.typicode.com/), a free, fake online REST API for testing and prototyping. It's perfect because it's simple and has a public OpenAPI specification.

-   **API Base URL:** `https://jsonplaceholder.typicode.com`
-   **OpenAPI Spec URL:** We'll use a community-provided spec for it.

## Step 2: Create the MCP Server

Now for the magic. We'll use `FastMCP.from_openapi`. This method takes an `httpx.AsyncClient` configured for your API and its OpenAPI specification, and automatically converts **every endpoint** into a callable MCP `Tool`.

<Tip>
Learn more about working with OpenAPI specs in the [OpenAPI integration docs](/servers/openapi).
</Tip>

<Note>
For this tutorial, we'll use a simplified OpenAPI spec directly in the code. In a real project, you would typically load the spec from a URL or local file.
</Note>

Create a file named `api_server.py`:

```python api_server.py {31-35}
import httpx
from fastmcp import FastMCP

# Create an HTTP client for the target API
client = httpx.AsyncClient(base_url="https://jsonplaceholder.typicode.com")

# Define a simplified OpenAPI spec for JSONPlaceholder
openapi_spec = {
    "openapi": "3.0.0",
    "info": {"title": "JSONPlaceholder API", "version": "1.0"},
    "paths": {
        "/users": {
            "get": {
                "summary": "Get all users",
                "operationId": "get_users",
                "responses": {"200": {"description": "A list of users."}}
            }
        },
        "/users/{id}": {
            "get": {
                "summary": "Get a user by ID",
                "operationId": "get_user_by_id",
                "parameters": [{"name": "id", "in": "path", "required": True, "schema": {"type": "integer"}}],
                "responses": {"200": {"description": "A single user."}}
            }
        }
    }
}

# Create the MCP server from the OpenAPI spec
mcp = FastMCP.from_openapi(
    openapi_spec=openapi_spec,
    client=client,
    name="JSONPlaceholder MCP Server"
)

if __name__ == "__main__":
    mcp.run(transport="http", port=8000)
```

And that's it! With just a few lines of code, you've created an MCP server that exposes the entire JSONPlaceholder API as a collection of tools.

## Step 3: Test the Generated Server

Let's verify that our new MCP server works. We can use the `fastmcp.Client` to connect to it and inspect its tools.

<Tip>
Learn more about the FastMCP client in the [client docs](/clients/client).
</Tip>

Create a separate file, `api_client.py`:

```python api_client.py {2, 6, 9, 16}
import asyncio
from fastmcp import Client

async def main():
    # Connect to the MCP server we just created
    async with Client("http://127.0.0.1:8000/mcp/") as client:
        
        # List the tools that were automatically generated
        tools = await client.list_tools()
        print("Generated Tools:")
        for tool in tools:
            print(f"- {tool.name}")
            
        # Call one of the generated tools
        print("\n\nCalling tool 'get_user_by_id'...")
        user = await client.call_tool("get_user_by_id", {"id": 1})
        print(f"Result:\n{user.data}")

if __name__ == "__main__":
    asyncio.run(main())
```

First, run your server:
```bash
python api_server.py
```

Then, in another terminal, run the client:
```bash
python api_client.py
```

You should see a list of generated tools (`get_users`, `get_user_by_id`) and the result of calling the `get_user_by_id` tool, which fetches data from the live JSONPlaceholder API.

![](/assets/images/tutorial-rest-api-result.png)


## Step 4: Customizing Route Maps

By default, FastMCP converts every API endpoint into an MCP `Tool`. This ensures maximum compatibility with contemporary LLM clients, many of which **only support the `tools` part of the MCP specification.**

However, for clients that support the full MCP spec, representing `GET` requests as `Resources` can be more semantically correct and efficient.

FastMCP allows users to customize this behavior using the concept of "route maps". A `RouteMap` is a mapping of an API route to an MCP type. FastMCP checks each API route against your custom maps in order. If a route matches a map, it's converted to the specified `mcp_type`. Any route that doesn't match your custom maps will fall back to the default behavior (becoming a `Tool`).

<Tip>
Learn more about route maps in the [OpenAPI integration docs](/servers/openapi#route-mapping).
</Tip>

Here’s how you can add custom route maps to turn `GET` requests into `Resources` and `ResourceTemplates` (if they have path parameters):

```python api_server_with_resources.py {3, 37-42}
import httpx
from fastmcp import FastMCP
from fastmcp.server.openapi import RouteMap, MCPType


# Create an HTTP client for the target API
client = httpx.AsyncClient(base_url="https://jsonplaceholder.typicode.com")

# Define a simplified OpenAPI spec for JSONPlaceholder
openapi_spec = {
    "openapi": "3.0.0",
    "info": {"title": "JSONPlaceholder API", "version": "1.0"},
    "paths": {
        "/users": {
            "get": {
                "summary": "Get all users",
                "operationId": "get_users",
                "responses": {"200": {"description": "A list of users."}}
            }
        },
        "/users/{id}": {
            "get": {
                "summary": "Get a user by ID",
                "operationId": "get_user_by_id",
                "parameters": [{"name": "id", "in": "path", "required": True, "schema": {"type": "integer"}}],
                "responses": {"200": {"description": "A single user."}}
            }
        }
    }
}

# Create the MCP server with custom route mapping
mcp = FastMCP.from_openapi(
    openapi_spec=openapi_spec,
    client=client,
    name="JSONPlaceholder MCP Server",
    route_maps=[
        # Map GET requests with path parameters (e.g., /users/{id}) to ResourceTemplate
        RouteMap(methods=["GET"], pattern=r".*\{.*\}.*", mcp_type=MCPType.RESOURCE_TEMPLATE),
        # Map all other GET requests to Resource
        RouteMap(methods=["GET"], mcp_type=MCPType.RESOURCE),
    ]
)

if __name__ == "__main__":
    mcp.run(transport="http", port=8000)
```
With this configuration:
- `GET /users/{id}` becomes a `ResourceTemplate`.
- `GET /users` becomes a `Resource`.
- Any `POST`, `PUT`, etc. endpoints would still become `Tools` by default.