> ## Documentation Index

> Fetch the complete documentation index at: /llms.txt

> Use this file to discover all available pages before exploring further.

Skip to main content
AI SDKs

# Gemini SDK 🤝 FastMCP

Connect FastMCP servers to the Google Gemini SDK

Google’s Gemini API includes built-in support for MCP servers in their Python and JavaScript SDKs, allowing you to connect directly to MCP servers and use their tools seamlessly with Gemini models.

##
​
Gemini Python SDK

Google’s Gemini Python SDK can use FastMCP clients directly.
Google’s MCP integration is currently experimental and available in the Python and JavaScript SDKs. The API automatically calls MCP tools when needed and can connect to both local and remote MCP servers.
Currently, Gemini’s MCP support only accesses tools from MCP servers—it queries the `list_tools` endpoint and exposes those functions to the AI. Other MCP features like resources and prompts are not currently supported.

###
​
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.
server.py

    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()

###
​
Call the Server

To use the Gemini API with MCP, you’ll need to install the Google Generative AI SDK:

    pip install google-genai

You’ll also need to authenticate with Google. You can do this by setting the `GEMINI_API_KEY` environment variable. Consult the Gemini SDK documentation for more information.

    export GEMINI_API_KEY="your-api-key"

Gemini’s SDK interacts directly with the MCP client session. To call the server, you’ll need to instantiate a FastMCP client, enter its connection context, and pass the client session to the Gemini SDK.

    from fastmcp import Client
    from google import genai
    import asyncio

    mcp_client = Client("server.py")
    gemini_client = genai.Client()

    async def main():
        async with mcp_client:
            response = await gemini_client.aio.models.generate_content(
                model="gemini-2.0-flash",
                contents="Roll 3 dice!",
                config=genai.types.GenerateContentConfig(
                    temperature=0,
                    tools=[mcp_client.session],  # Pass the FastMCP client session
                ),
            )
            print(response.text)

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

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

    Okay, I rolled 3 dice and got a 5, 4, and 1.

###
​
Remote & Authenticated Servers

In the above example, we connected to our local server using `stdio` transport. Because we’re using a FastMCP client, you can also connect to any local or remote MCP server, using any transport or auth method supported by FastMCP, simply by changing the client configuration. For example, to connect to a remote, authenticated server, you can use the following client:

    from fastmcp import Client
    from fastmcp.client.auth import BearerAuth

    mcp_client = Client(
        "https://my-server.com/mcp/",
        auth=BearerAuth("<your-token>"),
    )

The rest of the code remains the same.
Anthropic API 🤝 FastMCP
Previous
OpenAI API 🤝 FastMCP
Next
⌘I
Assistant
Responses are generated using AI and may contain mistakes.