Anthropic API + FastMCP

Anthropic’s Messages API supports MCP servers as remote tool sources. This tutorial will show you how to create a FastMCP server and deploy it to a public URL, then how to call it from the Messages API.

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Create a Server

First, create a FastMCP server with the tools you want to expose. For this example, we’ll create a server with a single tool that rolls dice.

import random
from fastmcp import FastMCP

mcp = FastMCP(name="Dice Roller")

@mcp.tool
def roll_dice(n_dice: int) -> list[int]:
    """Roll `n_dice` 6-sided dice and return the results."""
    return [random.randint(1, 6) for _ in range(n_dice)]

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

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Deploy the Server

Your server must be deployed to a public URL in order for Anthropic to access it. The MCP connector supports both SSE and Streamable HTTP transports.

For development, you can use tools like ngrok to temporarily expose a locally-running server to the internet. We’ll do that for this example (you may need to install ngrok and create a free account), but you can use any other method to deploy your server.

Assuming you saved the above code as server.py, you can run the following two commands in two separate terminals to deploy your server and expose it to the internet:

python server.py

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Call the Server

To use the Messages API with MCP servers, you’ll need to install the Anthropic Python SDK (not included with FastMCP):

pip install anthropic

You’ll also need to authenticate with Anthropic. You can do this by setting the ANTHROPIC_API_KEY environment variable. Consult the Anthropic SDK documentation for more information.

export ANTHROPIC_API_KEY="your-api-key"

Here is an example of how to call your server from Python. Note that you’ll need to replace https://your-server-url.com with the actual URL of your server. In addition, we use /sse as the endpoint because we deployed an SSE server with the default path; you may need to use a different endpoint if you customized your server’s deployment. At this time you must also include the extra_headers parameter with the anthropic-beta header.

import anthropic
from rich import print

# Your server URL (replace with your actual URL)
url = 'https://your-server-url.com'

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Roll a few dice!"}],
    mcp_servers=[
        {
            "type": "url",
            "url": f"{url}/sse",
            "name": "dice-server",
        }
    ],
    extra_headers={
        "anthropic-beta": "mcp-client-2025-04-04"
    }
)

print(response.content)

If you run this code, you’ll see something like the following output:

I'll roll some dice for you! Let me use the dice rolling tool.

I rolled 3 dice and got: 4, 2, 6

The results were 4, 2, and 6. Would you like me to roll again or roll a different number of dice?

​

Authentication

The MCP connector supports OAuth authentication through authorization tokens, which means you can secure your server while still allowing Anthropic to access it.

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Server Authentication

The simplest way to add authentication to the server is to use a bearer token scheme.

For this example, we’ll quickly generate our own tokens with FastMCP’s RSAKeyPair utility, but this may not be appropriate for production use. For more details, see the complete server-side Bearer Auth documentation.

We’ll start by creating an RSA key pair to sign and verify tokens.

from fastmcp.server.auth.providers.bearer import RSAKeyPair

key_pair = RSAKeyPair.generate()
access_token = key_pair.create_token(audience="dice-server")

Next, we’ll create a BearerAuthProvider to authenticate the server.

from fastmcp import FastMCP
from fastmcp.server.auth import BearerAuthProvider

auth = BearerAuthProvider(
    public_key=key_pair.public_key,
    audience="dice-server",
)

mcp = FastMCP(name="Dice Roller", auth=auth)

Here is a complete example that you can copy/paste. For simplicity and the purposes of this example only, it will print the token to the console. Do NOT do this in production!

from fastmcp import FastMCP
from fastmcp.server.auth import BearerAuthProvider
from fastmcp.server.auth.providers.bearer import RSAKeyPair
import random

key_pair = RSAKeyPair.generate()
access_token = key_pair.create_token(audience="dice-server")

auth = BearerAuthProvider(
    public_key=key_pair.public_key,
    audience="dice-server",
)

mcp = FastMCP(name="Dice Roller", auth=auth)

@mcp.tool
def roll_dice(n_dice: int) -> list[int]:
    """Roll `n_dice` 6-sided dice and return the results."""
    return [random.randint(1, 6) for _ in range(n_dice)]

if __name__ == "__main__":
    print(f"\n---\n\n🔑 Dice Roller access token:\n\n{access_token}\n\n---\n")
    mcp.run(transport="sse", port=8000)

​

Client Authentication

If you try to call the authenticated server with the same Anthropic code we wrote earlier, you’ll get an error indicating that the server rejected the request because it’s not authenticated.

Error code: 400 - {
    "type": "error", 
    "error": {
        "type": "invalid_request_error", 
        "message": "MCP server 'dice-server' requires authentication. Please provide an authorization_token.",
    },
}

To authenticate the client, you can pass the token using the authorization_token parameter in your MCP server configuration:

import anthropic
from rich import print

# Your server URL (replace with your actual URL)
url = 'https://your-server-url.com'

# Your access token (replace with your actual token)
access_token = 'your-access-token'

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Roll a few dice!"}],
    mcp_servers=[
        {
            "type": "url",
            "url": f"{url}/sse",
            "name": "dice-server",
            "authorization_token": access_token
        }
    ],
    extra_headers={
        "anthropic-beta": "mcp-client-2025-04-04"
    }
)

print(response.content)

You should now see the dice roll results in the output.