How to integrate Gmail MCP with Autogen

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Introduction

This guide walks you through connecting Gmail to AutoGen using the Composio tool router. By the end, you'll have a working Gmail agent that can read emails, search your inbox, draft messages, manage labels, and organize threads through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Gmail account through Composio's Gmail MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Gmail
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Gmail tools
  • Run a live chat loop where you ask the agent to perform Gmail operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Gmail MCP server, and what's possible with it?

The Gmail MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gmail account. It provides structured and secure access to your email, so your agent can search, read, draft, organize, and even manage contacts in your mailbox—all on your behalf.

  • Advanced email search and retrieval: Effortlessly instruct your agent to fetch emails by sender, subject, label, date, or keywords, and even retrieve full message content or threads.
  • Automated drafting and sending: Have your agent create new email drafts, craft replies, add CC/BCC, include attachments, and handle threading to streamline communication.
  • Smart label and inbox organization: Let the agent create new labels, apply or remove labels from emails, and keep your inbox clutter-free by archiving or moving messages.
  • Contact and thread management: Fetch your Gmail contacts, pull entire conversation threads, or download specific attachments to make follow-ups a breeze.
  • Email and draft cleanup: Direct your agent to permanently delete emails or drafts, helping you maintain a tidy mailbox with minimal effort.

Supported Tools & Triggers

Tools
Triggers
Modify email labelsAdds and/or removes specified gmail labels for a message; ensure `message id` and all `label ids` are valid (use 'listlabels' for custom label ids).
Create email draftCreates a gmail email draft, supporting to/cc/bcc, subject, plain/html body (ensure `is html=true` for html), attachments, and threading.
Create labelCreates a new label with a unique name in the specified user's gmail account.
Delete DraftPermanently deletes a specific gmail draft using its id; ensure the draft exists and the user has necessary permissions for the given `user id`.
Delete messagePermanently deletes a specific email message by its id from a gmail mailbox; for `user id`, use 'me' for the authenticated user or an email address to which the authenticated user has delegated access.
Fetch emailsFetches a list of email messages from a gmail account, supporting filtering, pagination, and optional full content retrieval.
Fetch message by message IDFetches a specific email message by its id, provided the `message id` exists and is accessible to the authenticated `user id`.
Fetch Message by Thread IDRetrieves messages from a gmail thread using its `thread id`, where the thread must be accessible by the specified `user id`.
Get Gmail attachmentRetrieves a specific attachment by id from a message in a user's gmail mailbox, requiring valid message and attachment ids.
Get contactsFetches contacts (connections) for the authenticated google account, allowing selection of specific data fields and pagination.
Get PeopleRetrieves either a specific person's details (using `resource name`) or lists 'other contacts' (if `other contacts` is true), with `person fields` specifying the data to return.
Get ProfileRetrieves key gmail profile information (email address, message/thread totals, history id) for a user.
List draftsRetrieves a paginated list of email drafts from a user's gmail account.
List Gmail labelsRetrieves a list of all system and user-created labels for the specified gmail account.
List threadsRetrieves a list of email threads from a gmail account, identified by `user id` (email address or 'me'), supporting filtering and pagination.
Modify thread labelsAdds or removes specified existing label ids from a gmail thread, affecting all its messages; ensure the thread id is valid.
Move to TrashMoves an existing, non-deleted email message to the trash for the specified user.
Patch LabelPatches the specified label.
Remove labelPermanently deletes a specific, existing user-created gmail label by its id for a user; cannot delete system labels.
Reply to email threadSends a reply within a specific gmail thread using the original thread's subject, requiring a valid `thread id` and correctly formatted email addresses.
Search PeopleSearches contacts by matching the query against names, nicknames, emails, phone numbers, and organizations, optionally including 'other contacts'.
Send DraftSends the specified, existing draft to the recipients in the to, cc, and bcc headers.
Send EmailSends an email via gmail api using the authenticated user's google profile display name, requiring `is html=true` if the body contains html and valid `s3key`, `mimetype`, `name` for any attachment.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Gmail account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Gmail via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Gmail connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Gmail session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["gmail"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Gmail tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Gmail assistant agent with MCP tools
    agent = AssistantAgent(
        name="gmail_assistant",
        description="An AI assistant that helps with Gmail operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Gmail tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Gmail related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Gmail tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Gmail and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Gmail session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["gmail"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Gmail assistant agent with MCP tools
        agent = AssistantAgent(
            name="gmail_assistant",
            description="An AI assistant that helps with Gmail operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Gmail related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Gmail through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Gmail, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Gmail MCP Agent with another framework

FAQ

What are the differences in Tool Router MCP and Gmail MCP?

With a standalone Gmail MCP server, the agents and LLMs can only access a fixed set of Gmail tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Gmail and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Gmail tools.

Can I manage the permissions and scopes for Gmail while using Tool Router?

Yes, absolutely. You can configure which Gmail scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Gmail data and credentials are handled as safely as possible.

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