> ## Documentation Index
> Fetch the complete documentation index at: https://gofastmcp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI API 🤝 FastMCP

> Connect FastMCP servers to the OpenAI API

export const VersionBadge = ({version}) => {
  return <Badge stroke size="lg" icon="gift" iconType="regular" className="version-badge">
            New in version <code>{version}</code>
        </Badge>;
};

## Responses API

OpenAI's [Responses API](https://platform.openai.com/docs/api-reference/responses) supports [MCP servers](https://platform.openai.com/docs/guides/tools-remote-mcp) as remote tool sources, allowing you to extend AI capabilities with custom functions.

<Note>
  The Responses API is a distinct API from OpenAI's Completions API or Assistants API. At this time, only the Responses API supports MCP.
</Note>

<Tip>
  Currently, the Responses API only accesses **tools** from MCP servers—it queries the `list_tools` endpoint and exposes those functions to the AI agent. Other MCP features like resources and prompts are not currently supported.
</Tip>

### 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.

```python server.py theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
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="http", port=8000)
```

### Deploy the Server

Your server must be deployed to a public URL in order for OpenAI to access it.

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:

<CodeGroup>
  ```bash FastMCP server theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
  python server.py
  ```

  ```bash ngrok theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
  ngrok http 8000
  ```
</CodeGroup>

<Warning>
  This exposes your unauthenticated server to the internet. Only run this command in a safe environment if you understand the risks.
</Warning>

### Call the Server

To use the Responses API, you'll need to install the OpenAI Python SDK (not included with FastMCP):

```bash theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
pip install openai
```

You'll also need to authenticate with OpenAI. You can do this by setting the `OPENAI_API_KEY` environment variable. Consult the OpenAI SDK documentation for more information.

```bash theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
export OPENAI_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 `/mcp/` as the endpoint because we deployed a streamable-HTTP server with the default path; you may need to use a different endpoint if you customized your server's deployment.

```python {4, 11-16} theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
from openai import OpenAI

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

client = OpenAI()

resp = client.responses.create(
    model="gpt-4.1",
    tools=[
        {
            "type": "mcp",
            "server_label": "dice_server",
            "server_url": f"{url}/mcp/",
            "require_approval": "never",
        },
    ],
    input="Roll a few dice!",
)

print(resp.output_text)
```

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

```text theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
You rolled 3 dice and got the following results: 6, 4, and 2!
```

### Authentication

<VersionBadge version="2.6.0" />

The Responses API can include headers to authenticate the request, which means you don't have to worry about your server being publicly accessible.

#### 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 [Token Verification](/servers/auth/token-verification) documentation.

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

```python theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
from fastmcp.server.auth.providers.jwt import RSAKeyPair

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

<Warning>
  FastMCP's `RSAKeyPair` utility is for development and testing only.
</Warning>

Next, we'll create a `JWTVerifier` to authenticate the server.

```python theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
from fastmcp import FastMCP
from fastmcp.server.auth import JWTVerifier

auth = JWTVerifier(
    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!**

```python server.py [expandable] theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
from fastmcp import FastMCP
from fastmcp.server.auth import JWTVerifier
from fastmcp.server.auth.providers.jwt import RSAKeyPair
import random

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

auth = JWTVerifier(
    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="http", port=8000)
```

#### Client Authentication

If you try to call the authenticated server with the same OpenAI code we wrote earlier, you'll get an error like this:

```text theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
APIStatusError: Error code: 424 - {
    "error": {
        "message": "Error retrieving tool list from MCP server: 'dice_server'. Http status code: 401 (Unauthorized)",
        "type": "external_connector_error",
        "param": "tools",
        "code": "http_error"
    }
}
```

As expected, the server is rejecting the request because it's not authenticated.

To authenticate the client, you can pass the token in the `Authorization` header with the `Bearer` scheme:

```python {4, 7, 19-21} [expandable] theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}}
from openai import OpenAI

# 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 = OpenAI()

resp = client.responses.create(
    model="gpt-4.1",
    tools=[
        {
            "type": "mcp",
            "server_label": "dice_server",
            "server_url": f"{url}/mcp/",
            "require_approval": "never",
            "headers": {
                "Authorization": f"Bearer {access_token}"
            }
        },
    ],
    input="Roll a few dice!",
)

print(resp.output_text)
```

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