How to integrate Google Tasks MCP with Autogen

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Introduction

This guide walks you through connecting Google Tasks to AutoGen using the Composio tool router. By the end, you'll have a working Google Tasks agent that can add a new task to my work list, list all tasks due this week, delete completed tasks from my shopping list, move an urgent task to the top of my personal list through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Google Tasks account through Composio's Google Tasks 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 Google Tasks
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Google Tasks tools
  • Run a live chat loop where you ask the agent to perform Google Tasks 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 Google Tasks MCP server, and what's possible with it?

The Google Tasks MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Google Tasks account. It provides structured and secure access to your to-do lists and tasks, so your agent can create task lists, add or update tasks, reorganize and clean up your lists, and fetch or manage your action items automatically.

  • Intelligent task list management: Ask your agent to create new to-do lists, fetch existing ones, or remove lists you no longer need—all without manual clicks.
  • Automated task creation and updates: Let your agent add new tasks, set due dates, or update existing to-dos to keep your lists current and organized.
  • Efficient task organization and movement: Move tasks between lists, reorder them, or set parent/child relationships so your priorities always stay clear.
  • Fast cleanup and deletion: Direct your agent to clear completed tasks or delete specific items and lists, helping you declutter swiftly and securely.
  • Detailed task retrieval and review: Have your agent pull details on any task or list so you can review upcoming deadlines, notes, and status at a glance.

Supported Tools & Triggers

Tools
Clear tasksPermanently clears all completed tasks from a specified google tasks list; this action is destructive and idempotent.
Create a task listCreates a new task list with the specified title.
Delete taskDeletes a specified task from a given task list in google tasks.
Delete task listPermanently deletes an existing google task list, identified by `tasklist id`, along with all its tasks; this operation is irreversible.
Get TaskUse to retrieve a specific google task if its `task id` and parent `tasklist id` are known.
Get task listRetrieves a specific task list from the user's google tasks if the `tasklist id` exists for the authenticated user.
Insert TaskCreates a new task in a given `tasklist id`, optionally as a subtask of an existing `task parent` or positioned after an existing `task previous` sibling, where both `task parent` and `task previous` must belong to the same `tasklist id` if specified.
List task listsFetches the authenticated user's task lists from google tasks; results may be paginated.
List TasksRetrieves tasks from a google tasks list; all date/time strings must be rfc3339 utc, and `showcompleted` must be true if `completedmin` or `completedmax` are specified.
Move TaskMoves the specified task to another position in the destination task list.
Patch TaskPartially updates an existing task (identified by `task id`) within a specific google task list (identified by `tasklist id`), modifying only the provided attributes from `taskinput` (e.
Patch task listUpdates the title of an existing google tasks task list.
Update TaskUpdates the specified task.
Update Task ListUpdates the authenticated user's specified task list.

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 Google Tasks 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 Google Tasks 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 Google Tasks 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 Google Tasks session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["googletasks"]
    )
    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 Google Tasks 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 Google Tasks assistant agent with MCP tools
    agent = AssistantAgent(
        name="googletasks_assistant",
        description="An AI assistant that helps with Google Tasks 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 Google Tasks 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 Google Tasks 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 Google Tasks 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 Google Tasks 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 Google Tasks session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["googletasks"]
    )
    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 Google Tasks assistant agent with MCP tools
        agent = AssistantAgent(
            name="googletasks_assistant",
            description="An AI assistant that helps with Google Tasks 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 Google Tasks 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 Google Tasks 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 Google Tasks, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Google Tasks MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Google Tasks MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Google Tasks MCP with Claude Agent SDK

Google ADK

Google ADK

Use Google Tasks MCP with Google ADK

LangChain

LangChain

Use Google Tasks MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Google Tasks MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Google Tasks MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Google Tasks MCP with LlamaIndex

CrewAI

CrewAI

Use Google Tasks MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Google Tasks MCP?

With a standalone Google Tasks MCP server, the agents and LLMs can only access a fixed set of Google Tasks tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Google Tasks 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 Google Tasks tools.

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

Yes, absolutely. You can configure which Google Tasks 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 Google Tasks data and credentials are handled as safely as possible.

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