URL: https://gofastmcp.com/integrations/gemini
Title: Gemini SDK 🤝 FastMCP - FastMCP

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Gemini Python SDK
Create a Server
Call the Server
Remote & Authenticated Servers
AI SDKs
Gemini SDK 🤝 FastMCP
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Connect FastMCP servers to the Google Gemini SDK
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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
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