How to integrate Google Tasks MCP with LangChain

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Introduction

This guide walks you through connecting Google Tasks to LangChain 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 LangChain 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
  • Connect your Google Tasks project to Composio
  • Create a Tool Router MCP session for Google Tasks
  • Initialize an MCP client and retrieve Google Tasks tools
  • Build a LangChain agent that can interact with Google Tasks
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Google Tasks functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Google Tasks tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Google Tasks
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['googletasks']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Google Tasks tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Google Tasks tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "googletasks-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Google Tasks MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Google Tasks tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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")

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Google Tasks and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['googletasks']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "googletasks-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    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")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Google Tasks through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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

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 LangChain?

Yes, you can. LangChain 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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