How to integrate Todoist MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Todoist to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Todoist agent that can add a high-priority task for today, create a new project called 'team offsite', close all completed tasks from this week, add a comment to my 'marketing plan' task through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Todoist account through Composio's Todoist 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 necessary dependencies
  • Initialize Composio and create a Tool Router session for Todoist
  • Configure an AI agent that can use Todoist as a tool
  • Run a live chat session where you can ask the agent to perform Todoist operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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

The Todoist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Todoist account. It provides structured and secure access to your tasks, projects, and labels, so your agent can create tasks, manage projects, add comments, organize sections, and update your to-do lists on your behalf.

  • Task creation and scheduling: Instantly ask your agent to add new tasks with specific details, deadlines, priorities, or even as subtasks within projects or sections.
  • Project and workspace management: Let your agent create, organize, or delete projects and workspaces to keep your productivity system tidy and up-to-date.
  • Section and label organization: Direct your agent to create, delete, or update sections and labels, helping you structure your tasks and filter lists for better focus.
  • Task completion and commenting: Have your agent mark tasks as complete or add helpful comments and notes to specific tasks or projects for seamless collaboration.
  • Streamlined cleanup and maintenance: Empower your agent to remove unused projects, labels, or sections, ensuring your Todoist stays clutter-free and organized.

Supported Tools & Triggers

Tools
Triggers
Add WorkspaceTool to create a new workspace in todoist.
Close TaskThis tool marks an existing task as completed in todoist.
Create CommentTool to create a new comment in todoist.
Create LabelCreates a new label.
Create ProjectCreates a new project in todoist.
Create SectionTool to create a new section within a specific project.
Create taskCreate a new task in todoist.
Delete LabelTool to delete a specific label.
Delete ProjectTool to delete a specific todoist project.
Delete SectionTool to delete a specific section.
Delete taskDelete a task from todoist.
Get All CommentsThis tool retrieves all comments associated with a specific task or project in todoist.
Get all labelsGet all personal labels from todoist.
Get all projectsGet all projects from a user's todoist account.
Get All SectionsTool to retrieve all sections for a specific project in todoist.
Get All TasksFetches all tasks from todoist and returns their details.
Get BackupsTool to list all available backup archives for the user.
Get CommentTool to retrieve details of a specific comment by its comment id.
Get LabelTool to retrieve a specific label by its id.
Get ProjectTool to retrieve a specific project by its id.
Get SectionTool to retrieve a specific section by its id.
Get Special BackupsTool to list special backup archives for the user.
Get TaskTool to retrieve a specific task by its id.
List Archived Workspace ProjectsTool to list all archived projects in a workspace.
List FiltersTool to list all filters for the authenticated user.
List Pending Workspace InvitationsTool to list pending invitation emails in a workspace.
Reopen TaskThis tool reopens a previously completed task.
Update CommentTool to update a specific comment's content.
Update ProjectTool to update a specific project's attributes such as name, color, indent, and order.
Update SectionTool to update a specific section's attributes such as name and order.
Update TaskTool to update an existing task's properties.

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, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Todoist project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Todoist.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Todoist Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["todoist"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only todoist.
  • The router checks the user's Todoist connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Todoist.
  • This approach keeps things lightweight and lets the agent request Todoist tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Todoist. "
        "Help users perform Todoist operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Todoist and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Todoist operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Todoist.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Todoist and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["todoist"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Todoist. "
        "Help users perform Todoist operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Todoist MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Todoist.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build Todoist MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use Todoist MCP with Claude Agent SDK

Google ADK

Google ADK

Use Todoist MCP with Google ADK

LangChain

LangChain

Use Todoist MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Todoist MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Todoist MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Todoist MCP with LlamaIndex

CrewAI

CrewAI

Use Todoist MCP with CrewAI

FAQ

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

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

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Todoist tools.

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

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

Used by agents from

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