docs/integrations/fastapi.mdx

---
title: FastAPI 🤝 FastMCP
sidebarTitle: FastAPI
description: Integrate FastMCP with FastAPI applications
icon: bolt
---

import { VersionBadge } from '/snippets/version-badge.mdx'

<Tip>
**New in 2.11**: FastMCP is introducing a next-generation OpenAPI parser. The new parser has greatly improved performance and compatibility, and is also easier to maintain. To enable it, set the environment variable `FASTMCP_EXPERIMENTAL_ENABLE_NEW_OPENAPI_PARSER=true`. 

The new parser is largely API-compatible with the existing implementation and will become the default in a future version. We encourage all users to test it and report any issues before it becomes the default.
</Tip>

FastMCP provides two powerful ways to integrate with FastAPI applications:

1. **[Generate an MCP server FROM your FastAPI app](#generating-an-mcp-server)** - Convert existing API endpoints into MCP tools
2. **[Mount an MCP server INTO your FastAPI app](#mounting-an-mcp-server)** - Add MCP functionality to your web application


<Tip>
Generating MCP servers from OpenAPI is a great way to get started with FastMCP, but in practice LLMs achieve **significantly better performance** with well-designed and curated MCP servers than with auto-converted OpenAPI servers. This is especially true for complex APIs with many endpoints and parameters.

We recommend using the FastAPI integration for bootstrapping and prototyping, not for mirroring your API to LLM clients. See the post [Stop Converting Your REST APIs to MCP](https://www.jlowin.dev/blog/stop-converting-rest-apis-to-mcp) for more details.
</Tip>


<Note>
FastMCP does *not* include FastAPI as a dependency; you must install it separately to use this integration.
</Note>

## Example FastAPI Application

Throughout this guide, we'll use this e-commerce API as our example (click the `Copy` button to copy it for use with other code blocks):

```python [expandable]
# Copy this FastAPI server into other code blocks in this guide

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

# Models
class Product(BaseModel):
    name: str
    price: float
    category: str
    description: str | None = None

class ProductResponse(BaseModel):
    id: int
    name: str
    price: float
    category: str
    description: str | None = None

# Create FastAPI app
app = FastAPI(title="E-commerce API", version="1.0.0")

# In-memory database
products_db = {
    1: ProductResponse(
        id=1, name="Laptop", price=999.99, category="Electronics"
    ),
    2: ProductResponse(
        id=2, name="Mouse", price=29.99, category="Electronics"
    ),
    3: ProductResponse(
        id=3, name="Desk Chair", price=299.99, category="Furniture"
    ),
}
next_id = 4

@app.get("/products", response_model=list[ProductResponse])
def list_products(
    category: str | None = None,
    max_price: float | None = None,
) -> list[ProductResponse]:
    """List all products with optional filtering."""
    products = list(products_db.values())
    if category:
        products = [p for p in products if p.category == category]
    if max_price:
        products = [p for p in products if p.price <= max_price]
    return products

@app.get("/products/{product_id}", response_model=ProductResponse)
def get_product(product_id: int):
    """Get a specific product by ID."""
    if product_id not in products_db:
        raise HTTPException(status_code=404, detail="Product not found")
    return products_db[product_id]

@app.post("/products", response_model=ProductResponse)
def create_product(product: Product):
    """Create a new product."""
    global next_id
    product_response = ProductResponse(id=next_id, **product.model_dump())
    products_db[next_id] = product_response
    next_id += 1
    return product_response

@app.put("/products/{product_id}", response_model=ProductResponse)
def update_product(product_id: int, product: Product):
    """Update an existing product."""
    if product_id not in products_db:
        raise HTTPException(status_code=404, detail="Product not found")
    products_db[product_id] = ProductResponse(
        id=product_id,
        **product.model_dump(),
    )
    return products_db[product_id]

@app.delete("/products/{product_id}")
def delete_product(product_id: int):
    """Delete a product."""
    if product_id not in products_db:
        raise HTTPException(status_code=404, detail="Product not found")
    del products_db[product_id]
    return {"message": "Product deleted"}
```

<Tip>
All subsequent code examples in this guide assume you have the above FastAPI application code already defined. Each example builds upon this base application, `app`.
</Tip>

## Generating an MCP Server

<VersionBadge version="2.0.0" />

One of the most common ways to bootstrap an MCP server is to generate it from an existing FastAPI application. FastMCP will expose your FastAPI endpoints as MCP components (tools, by default) in order to expose your API to LLM clients.



### Basic Conversion

Convert the FastAPI app to an MCP server with a single line:

```python {5}
# Assumes the FastAPI app from above is already defined
from fastmcp import FastMCP

# Convert to MCP server
mcp = FastMCP.from_fastapi(app=app)

if __name__ == "__main__":
    mcp.run()
```

### Adding Components

Your converted MCP server is a full FastMCP instance, meaning you can add new tools, resources, and other components to it just like you would with any other FastMCP instance.

```python {8-11}
# Assumes the FastAPI app from above is already defined
from fastmcp import FastMCP

# Convert to MCP server
mcp = FastMCP.from_fastapi(app=app)

# Add a new tool
@mcp.tool
def get_product(product_id: int) -> ProductResponse:
    """Get a product by ID."""
    return products_db[product_id]

# Run the MCP server
if __name__ == "__main__":
    mcp.run()
```





### Interacting with the MCP Server

Once you've converted your FastAPI app to an MCP server, you can interact with it using the FastMCP client to test functionality before deploying it to an LLM-based application.

```python {3, }
# Assumes the FastAPI app from above is already defined
from fastmcp import FastMCP
from fastmcp.client import Client
import asyncio

# Convert to MCP server
mcp = FastMCP.from_fastapi(app=app)

async def demo():
    async with Client(mcp) as client:
        # List available tools
        tools = await client.list_tools()
        print(f"Available tools: {[t.name for t in tools]}")
        
        # Create a product
        result = await client.call_tool(
            "create_product_products_post",
            {
                "name": "Wireless Keyboard",
                "price": 79.99,
                "category": "Electronics",
                "description": "Bluetooth mechanical keyboard"
            }
        )
        print(f"Created product: {result.data}")
        
        # List electronics under $100
        result = await client.call_tool(
            "list_products_products_get",
            {"category": "Electronics", "max_price": 100}
        )
        print(f"Affordable electronics: {result.data}")

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

### Custom Route Mapping

Because FastMCP's FastAPI integration is based on its [OpenAPI integration](/integrations/openapi), you can customize how endpoints are converted to MCP components in exactly the same way. For example, here we use a `RouteMap` to map all GET requests to MCP resources, and all POST/PUT/DELETE requests to MCP tools:

```python
# Assumes the FastAPI app from above is already defined
from fastmcp import FastMCP
from fastmcp.server.openapi import RouteMap, MCPType

# If using experimental parser, import from experimental module:
# from fastmcp.experimental.server.openapi import RouteMap, MCPType

# Custom mapping rules
mcp = FastMCP.from_fastapi(
    app=app,
    route_maps=[
        # GET with path params → ResourceTemplates
        RouteMap(
            methods=["GET"], 
            pattern=r".*\{.*\}.*", 
            mcp_type=MCPType.RESOURCE_TEMPLATE
        ),
        # Other GETs → Resources
        RouteMap(
            methods=["GET"], 
            pattern=r".*", 
            mcp_type=MCPType.RESOURCE
        ),
        # POST/PUT/DELETE → Tools (default)
    ],
)

# Now:
# - GET /products → Resource
# - GET /products/{id} → ResourceTemplate
# - POST/PUT/DELETE → Tools
```

<Tip>
To learn more about customizing the conversion process, see the [OpenAPI Integration guide](/integrations/openapi).
</Tip>

### Authentication and Headers

You can configure headers and other client options via the `httpx_client_kwargs` parameter. For example, to add authentication to your FastAPI app, you can pass a `headers` dictionary to the `httpx_client_kwargs` parameter:

```python {27-31}
# Assumes the FastAPI app from above is already defined
from fastmcp import FastMCP

# Add authentication to your FastAPI app
from fastapi import Depends, Header
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials

security = HTTPBearer()

def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
    if credentials.credentials != "secret-token":
        raise HTTPException(status_code=401, detail="Invalid authentication")
    return credentials.credentials

# Add a protected endpoint
@app.get("/admin/stats", dependencies=[Depends(verify_token)])
def get_admin_stats():
    return {
        "total_products": len(products_db),
        "categories": list(set(p.category for p in products_db.values()))
    }

# Create MCP server with authentication headers
mcp = FastMCP.from_fastapi(
    app=app,
    httpx_client_kwargs={
        "headers": {
            "Authorization": "Bearer secret-token",
        }
    }
)
```

## Mounting an MCP Server

<VersionBadge version="2.3.1" />

In addition to generating servers, FastMCP can facilitate adding MCP servers to your existing FastAPI application. You can do this by mounting the MCP ASGI application.

### Basic Mounting

To mount an MCP server, you can use the `http_app` method on your FastMCP instance. This will return an ASGI application that can be mounted to your FastAPI application.

```python {23-30}
from fastmcp import FastMCP
from fastapi import FastAPI

# Create MCP server
mcp = FastMCP("Analytics Tools")

@mcp.tool
def analyze_pricing(category: str) -> dict:
    """Analyze pricing for a category."""
    products = [p for p in products_db.values() if p.category == category]
    if not products:
        return {"error": f"No products in {category}"}
    
    prices = [p.price for p in products]
    return {
        "category": category,
        "avg_price": round(sum(prices) / len(prices), 2),
        "min": min(prices),
        "max": max(prices),
    }

# Create ASGI app from MCP server
mcp_app = mcp.http_app(path='/mcp')

# Key: Pass lifespan to FastAPI
app = FastAPI(title="E-commerce API", lifespan=mcp_app.lifespan)

# Mount the MCP server
app.mount("/analytics", mcp_app)

# Now: API at /products/*, MCP at /analytics/mcp/
```

## Offering an LLM-Friendly API

A common pattern is to generate an MCP server from your FastAPI app and mount it back into the same application. This provides an LLM-optimized interface alongside your regular API:

```python
# Assumes the FastAPI app from above is already defined
from fastmcp import FastMCP
from fastapi import FastAPI

# 1. Generate MCP server from your API
mcp = FastMCP.from_fastapi(app=app, name="E-commerce MCP")

# 2. Create the MCP's ASGI app
mcp_app = mcp.http_app(path='/mcp')

# 3. Mount it back into your FastAPI app
app = FastAPI(title="E-commerce API", lifespan=mcp_app.lifespan)
app.mount("/llm", mcp_app)

# Now you have:
# - Regular API: http://localhost:8000/products
# - LLM-friendly MCP: http://localhost:8000/llm/mcp/
# Both served from the same FastAPI application!
```

This approach lets you maintain a single codebase while offering both traditional REST endpoints and MCP-compatible endpoints for LLM clients.

## Key Considerations

### Operation IDs

FastAPI operation IDs become MCP component names. Always specify meaningful operation IDs:

```python
# Good - explicit operation_id
@app.get("/users/{user_id}", operation_id="get_user_by_id")
def get_user(user_id: int):
    return {"id": user_id}

# Less ideal - auto-generated name
@app.get("/users/{user_id}")
def get_user(user_id: int):
    return {"id": user_id}
```

### Lifespan Management

When mounting MCP servers, always pass the lifespan context:

```python
# Correct - lifespan passed
mcp_app = mcp.http_app(path='/mcp')
app = FastAPI(lifespan=mcp_app.lifespan)
app.mount("/mcp", mcp_app)

# Incorrect - missing lifespan
app = FastAPI()
app.mount("/mcp", mcp.http_app())  # Session manager won't initialize
```

### Combining Lifespans

If your FastAPI app already has a lifespan (for database connections, startup tasks, etc.), you can't simply replace it with the MCP lifespan. Instead, you need to create a new lifespan function that manages both contexts. This ensures that both your app's initialization logic and the MCP server's session manager run properly:

```python
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastmcp import FastMCP

# Your existing lifespan
@asynccontextmanager
async def app_lifespan(app: FastAPI):
    # Startup
    print("Starting up the app...")
    # Initialize database, cache, etc.
    yield
    # Shutdown
    print("Shutting down the app...")

# Create MCP server
mcp = FastMCP("Tools")
mcp_app = mcp.http_app(path='/mcp')

# Combine both lifespans
@asynccontextmanager
async def combined_lifespan(app: FastAPI):
    # Run both lifespans
    async with app_lifespan(app):
        async with mcp_app.lifespan(app):
            yield

# Use the combined lifespan
app = FastAPI(lifespan=combined_lifespan)
app.mount("/mcp", mcp_app)
```

This pattern ensures both your app's initialization logic and the MCP server's session manager are properly managed. The key is using nested `async with` statements - the inner context (MCP) will be initialized after the outer context (your app), and cleaned up before it. This maintains the correct initialization and cleanup order for all your resources.

### Performance Tips

1. **Use in-memory transport for testing** - Pass MCP servers directly to clients
2. **Design purpose-built MCP tools** - Better than auto-converting complex APIs
3. **Keep tool parameters simple** - LLMs perform better with focused interfaces

For more details on configuration options, see the [OpenAPI Integration guide](/integrations/openapi).