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Using mcp-use with Google

The Google adapter allows you to seamlessly integrate tools, resources, and prompts from any MCP server with the Google Python SDK. This enables you to use mcp-use as a comprehensive tool provider for your Google-powered agents.

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How it Works

The GoogleMCPAdapter converts not only tools but also resources and prompts from your active MCP servers into a format compatible with Google’s tool-calling feature. It maps each of these MCP constructs to a callable function that the Google model can request.
  • Tools are converted directly to Google functions.
  • Resources are converted into functions that take no arguments and read the resource’s content.
  • Prompts are converted into functions that accept the prompt’s arguments.
The adapter maintains a mapping of these generated functions to their actual execution logic, allowing you to easily call them when requested by the model.

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Step-by-Step Guide

Here’s how to use the adapter to provide MCP tools, resources, and prompts to a Google Chat Completion.
Before starting, install the Google GenAI SDK:
uv pip install google-genai
1

First, set up your MCPClient with the desired MCP servers. This part of the process is the same as any other mcp-use application.
from mcp_use import MCPClient

config = {
    "mcpServers": {"playwright": {"command": "npx", "args": ["@playwright/mcp@latest"], "env": {"DISPLAY": ":1"}}}
}

client = MCPClient(config=config)
2

Next, instantiate the GoogleMCPAdapter. This adapter will be responsible for converting MCP constructs into a format Google can understand.
from mcp_use.adapters import GoogleMCPAdapter

# Creates the adapter for Google's format
adapter = GoogleMCPAdapter()
You can pass a disallowed_tools list to the adapter’s constructor to prevent specific tools, resources, or prompts from being exposed to the model.
3

Use the create_all method on the adapter to inspect all connected MCP servers and generate a list of tools, resources and prompts in the Google function-calling format.
from google.genai import types

# Convert tools from active connectors to the Google's format
# this will populates the list of tools, resources and prompts
await adapter.create_all(client)

# If you decided to create all tools (list concatenation)
all_tools = adapter.tools + adapter.resources + adapter.prompts
google_tools = [types.Tool(function_declarations=all_tools)]
This list will include functions generated from your MCP tools, resources, and prompts.
If you don’t want to create all tools, you can call single functions. For example, if you only want to use tools and resources, you can do the following:
await adapter.create_tools(client)
await adapter.create_resources(client)

# Then, you can decide which ones to use:
active_tools = adapter.tools + adapter.resources
google_tools = [types.Tool(function_declarations=active_tools)]
4

Now, you can use the generated google_tools in a call to the Google API. The model will use the descriptions of these tools to decide if it needs to call any of them to answer the user’s query.
from google import genai
from google.genai import types

gemini = genai.Client()

messages = [
    types.Content(
        role="user",
        parts=[
            types.Part.from_text(
                text="Please search on the internet using browser: 'What time is it in Favignana now!'"
            )
        ],
    )
]
# Initial request
response = gemini.models.generate_content(
    model="gemini-flash-lite-latest", contents=messages, config=types.GenerateContentConfig(tools=google_tools)
)
5

If the model decides to use one or more tools, you need to iterate through the function calls, execute the corresponding functions, and append the results to your message history.The GoogleMCPAdapter makes this easy by providing a tool_executors dictionary and a parse_result method.
# Do multiple tool calls if needed
while response.function_calls:
    for function_call in response.function_calls:
        function_call_content = response.candidates[0].content

        messages.append(function_call_content)

        tool_name = function_call.name
        arguments = function_call.args

        # 1. Use the adapter's map to get the correct executor
        executor = adapter.tool_executors.get(tool_name)

        if not executor:
            function_response_content = types.Content(
                role="tool",
                parts=[
                    types.Part.from_function_response(
                        name=tool_name,
                        response={"error": "No executor found for the tool requested"},
                    )
                ],
            )
        else:
            try:
                # 2. Execute the tool using the retrieved function
                print(f"Executing tool: {tool_name}({arguments})")
                tool_result = await executor(**arguments)

                # 3. Use the adapter's universal parser
                content = adapter.parse_result(tool_result)
                function_response = {"result": content}

                # Build function response message
                function_response_part = types.Part.from_function_response(
                    name=tool_name,
                    response=function_response,
                )
                function_response_content = types.Content(role="tool", parts=[function_response_part])
            except Exception as e:
                function_response_content = types.Content(
                    role="tool",
                    parts=[
                        types.Part.from_function_response(
                            name=tool_name,
                            response={"error": str(e)},
                        )
                    ],
                )
        # 4. Append the tool's result to the conversation history
        messages.append(function_response_content)
The adapter.parse_result(tool_result) method simplifies the process by correctly formatting the output, whether it’s from a standard tool, a resource, or a prompt.
6

Finally, send the updated message history which now includes the tool call results back to the model. This allows the model to use the information gathered from the tools to formulate its final answer.
# Send the tool's result back to the model to get the next response
response = gemini.models.generate_content(
    model="gemini-flash-lite-latest",
    contents=messages,
    config=types.GenerateContentConfig(tools=google_tools),
)

# Get final response, the loop has finished
print("\n--- Final response from the model ---")
if response.text:
    print(response.text)

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Complete Example

For reference, here is the complete, runnable code for integrating mcp-use with the Google SDK. 
import asyncio

from dotenv import load_dotenv
from google import genai
from google.genai import types

from mcp_use import MCPClient
from mcp_use.adapters import GoogleMCPAdapter

# This example demonstrates how to use our integration
# adapters to use MCP tools and convert to the right format.
# In particularly, this example uses the GoogleMCPAdapter.

load_dotenv()


async def main():
    config = {
        "mcpServers": {"playwright": {"command": "npx", "args": ["@playwright/mcp@latest"], "env": {"DISPLAY": ":1"}}}
    }

    try:
        client = MCPClient(config=config)

        # Creates the adapter for Google's format
        adapter = GoogleMCPAdapter()

        # Convert tools from active connectors to Google's format
        await adapter.create_all(client)

        # List concatenation (if you loaded all tools)
        all_tools = adapter.tools + adapter.resources + adapter.prompts
        google_tools = [types.Tool(function_declarations=all_tools)]

        # If you don't want to create all tools, you can call single functions
        # await adapter.create_tools(client)
        # await adapter.create_resources(client)
        # await adapter.create_prompts(client)

        # Use tools with Google's SDK (not agent in this case)
        gemini = genai.Client()

        messages = [
            types.Content(
                role="user",
                parts=[
                    types.Part.from_text(
                        text="Please search on the internet using browser: 'What time is it in Favignana now!'"
                    )
                ],
            )
        ]
        # Initial request
        response = gemini.models.generate_content(
            model="gemini-flash-lite-latest", contents=messages, config=types.GenerateContentConfig(tools=google_tools)
        )

        if not response.function_calls:
            print("The model didn't do any tool call!")
            return

        # Do multiple tool calls if needed
        while response.function_calls:
            for function_call in response.function_calls:
                function_call_content = response.candidates[0].content

                messages.append(function_call_content)

                tool_name = function_call.name
                arguments = function_call.args

                # Use the adapter's map to get the correct executor
                executor = adapter.tool_executors.get(tool_name)

                if not executor:
                    print(f"Error: Unknown tool '{tool_name}' requested by model.")
                    function_response_content = types.Content(
                        role="tool",
                        parts=[
                            types.Part.from_function_response(
                                name=tool_name,
                                response={"error": "No executor found for the tool requested"},
                            )
                        ],
                    )
                else:
                    try:
                        # Execute the tool using the retrieved function
                        print(f"Executing tool: {tool_name}({arguments})")
                        tool_result = await executor(**arguments)

                        # Use the adapter's universal parser
                        content = adapter.parse_result(tool_result)
                        function_response = {"result": content}

                        # Build function response message
                        function_response_part = types.Part.from_function_response(
                            name=tool_name,
                            response=function_response,
                        )
                        function_response_content = types.Content(role="tool", parts=[function_response_part])
                    except Exception as e:
                        print(f"An unexpected error occurred while executing tool {tool_name}: {e}")
                        function_response_content = types.Content(
                            role="tool",
                            parts=[
                                types.Part.from_function_response(
                                    name=tool_name,
                                    response={"error": str(e)},
                                )
                            ],
                        )
                # Append the tool's result to the conversation history
                messages.append(function_response_content)
                # Send the tool's result back to the model to get the next response

            response = gemini.models.generate_content(
                model="gemini-flash-lite-latest",
                contents=messages,
                config=types.GenerateContentConfig(tools=google_tools),
            )

        # Get final response, the loop has finished
        print("\n--- Final response from the model ---")
        if response.text:
            print(response.text)
        else:
            print("The model did not return a final text response.")
            print(response)

        gemini.close()
    except Exception as e:
        print(f"Error: {e}")
        raise e


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