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Gemini supports MCP servers natively through the google-genai Python SDK — no FastMCP wrapper required. Google currently labels their MCP integration as experimental, so the exact API surface may evolve over time.
The recommended way to connect Gemini to the Statista MCP server is to use the official mcp Python package’s streamable HTTP transport, then pass the resulting MCP session directly to Gemini as a tool.

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Install dependencies

pip install google-genai mcp

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Connect via the google-genai SDK

from google import genai
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

mcp_server_url = "<PROVIDED_MCP_SERVER_URL>"
YOUR_STATISTA_API_KEY = "<YOUR_STATISTA_API_KEY>"
YOUR_GEMINI_API_KEY = "<YOUR_GEMINI_API_KEY>"

async with streamablehttp_client(
    mcp_server_url,
    headers={"x-api-key": YOUR_STATISTA_API_KEY},
) as (read_stream, write_stream, _):
    async with ClientSession(read_stream, write_stream) as session:
        await session.initialize()

        client = genai.Client(api_key=YOUR_GEMINI_API_KEY)
        response = await client.aio.models.generate_content(
            model="gemini-3.5-flash",
            contents="What is the ice cream market in Japan?",
            config=genai.types.GenerateContentConfig(
                temperature=0,
                tools=[session],
            ),
        )
        print(response.text)
The ClientSession object is passed directly into the tools list — Gemini introspects the MCP session’s available tools and invokes them automatically as needed.

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Alternative: using FastMCP

If you’re already using fastmcp elsewhere in your stack, you can pass its session to Gemini the same way. This is functionally equivalent to the native pattern above, just with FastMCP’s higher-level wrapper. 
from google import genai
from fastmcp import Client
from fastmcp.client.transports import StreamableHttpTransport

mcp_client = Client(
    transport=StreamableHttpTransport(
        mcp_server_url,
        headers={"x-api-key": YOUR_STATISTA_API_KEY},
    ),
)

client = genai.Client(api_key=YOUR_GEMINI_API_KEY)

async with mcp_client:
    response = await client.aio.models.generate_content(
        model="gemini-3.5-flash",
        contents="What is the ice cream market in Japan?",
        config=genai.types.GenerateContentConfig(
            temperature=0,
            tools=[mcp_client.session],
        ),
    )
    print(response.text)