How to integrate Todoist MCP with LlamaIndex

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Introduction

This guide walks you through connecting Todoist to LlamaIndex 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 through natural language commands.

This guide will help you understand how to give your LlamaIndex 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.

Also integrate Todoist with

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Todoist
  • Connect LlamaIndex to the Todoist MCP server
  • Build a Todoist-powered agent using LlamaIndex
  • Interact with Todoist through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

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.
Archive Project (API v1)Tool to archive a project using Todoist API v1.
Bulk Create TasksCreate many tasks in one request using Todoist's Sync batching.
Close Task (API v1)Tool to close (complete) a task in Todoist using API v1.
Create Comment (API v1)Tool to create a new comment on a project or task using Todoist API v1.
Create Label (API v1)Tool to create a new personal label using API v1.
Create Project (API v1)Tool to create a new project in Todoist using the unified API v1.
Create Section (API v1)Tool to create a new section within a project using API v1.
Create taskCreate a new task in Todoist using the unified API v1.
Delete CommentTool to delete a specific comment from Todoist by its ID.
Delete Label (V1)Tool to delete a personal label using API v1.
Delete Project (API v1)Tool to delete a project and all of its sections and tasks using Todoist API v1.
Delete Section (v1)Tool to delete a section and all tasks within it.
Delete TaskTool to delete a specific task from Todoist.
Delete UploadTool to delete an uploaded file from Todoist.
Export Template As FileTool to export a Todoist project as a CSV template file.
Export Template As URLTool to export a Todoist project as a shareable template URL.
Filter TasksTool to get all tasks matching the filter.
Get All CommentsThis tool retrieves all comments associated with a specific task or project in Todoist.
Get all projectsGet all projects from a user's Todoist account.
Get All TasksFetches all INCOMPLETE tasks from Todoist and returns their details.
Get BackupsTool to list all available backup archives for the user.
Get Comment (V1)Tool to retrieve a single comment by ID using the v1 API.
Get Completed Tasks By Completion DateTool to retrieve completed tasks within a specified completion date window.
Get ID MappingsTool to translate IDs between Todoist API v1 and v2.
Get Personal LabelTool to retrieve a personal label by its ID.
Get Productivity StatsTool to retrieve comprehensive productivity statistics for the authenticated user.
Get Project (API v1)Tool to retrieve a specific project by its ID using Todoist API v1.
Get Full Project DataTool to retrieve full project data including all sections, tasks, and collaborators.
Get Project PermissionsTool to retrieve all available roles and their associated actions in Todoist projects.
Get Section (v1 API)Tool to retrieve a specific section by its ID using Todoist v1 API.
Get Special BackupsTool to list special backup archives for the authenticated user's projects.
Get Task (API v1)Tool to retrieve a single active (non-completed) task by ID using API v1.
Get UserTool to retrieve information about the currently authenticated user.
Get Workspace Plan DetailsTool to retrieve details about a workspace's current plan and usage.
Import Template Into Project By IDTool to import a template from Todoist's template gallery into an existing project.
Import Template Into Project From FileTool to import a CSV template into an existing Todoist project from a file.
Invite Project CollaboratorTool to invite a collaborator to a Todoist project by email.
List ActivitiesTool to get activity logs from Todoist.
List All Workspace InvitationsTool to return a list containing details of all pending invitations to a workspace.
List Archived ProjectsTool to get all archived projects from Todoist.
List Archived SectionsTool to retrieve all archived sections for a specific project in Todoist.
List Archived Workspace ProjectsTool to list all archived projects in a workspace.
List Completed TasksTool to retrieve all completed tasks with optional project filtering.
List Completed Tasks By Due DateTool to retrieve completed tasks within a specified due date range (up to 6 weeks).
List FiltersTool to list all filters for the authenticated user.
List Joinable WorkspacesTool to get workspaces the user can join.
List LabelsTool to get all user labels with pagination support.
List Pending Workspace InvitationsTool to list pending invitation emails in a workspace.
List Project CollaboratorsTool to get all collaborators for a given project with cursor-based pagination.
List SectionsTool to get all active sections for the user, with optional filtering by project.
List Shared LabelsTool to retrieve shared label names from active tasks with pagination support.
List Workspace Active ProjectsTool to list all active workspace projects.
List Workspace Archived ProjectsTool to get archived projects in a workspace.
List Workspace InvitationsTool to list user emails with pending invitations to a workspace.
List Workspace UsersTool to list users in workspace(s).
Move TaskTool to move a task to another project, section, or parent task while preserving task identity and metadata.
Move Task (REST API)Tool to move a task to another project, section, or parent task using the REST API.
Quick Add TaskTool to add tasks using natural language parsing similar to the official Todoist clients.
Remove Shared Label (API v1)Tool to remove a shared label from all active tasks using API v1.
Rename Shared Labels (API v1)Tool to rename a shared label across all active tasks using API v1.
Reopen Task (API v1)Tool to reopen a completed task in Todoist using API v1.
Reorder TasksReorder tasks deterministically by updating child_order in bulk via the Sync API item_reorder command.
Search LabelsTool to search user labels by name with case-insensitive matching.
Search ProjectsSearch active user projects by name with support for wildcards and pagination.
Search SectionsTool to search active sections by name, optionally filtered by project.
Todoist SyncTool to sync data with Todoist server, supporting both read and write operations.
Unarchive Project (API v1)Tool to unarchive a previously archived Todoist project using API v1.
Update Comment (v1)Tool to update a comment by ID and return its content via v1 API.
Update Label (API v1)Tool to update an existing label using API v1.
Update Notification SettingTool to update notification settings for the current user.
Update Project (API v1)Tool to update a project's properties using Todoist API v1.
Update Section (v1)Tool to update an existing section by its ID using Todoist v1 API.
Update TaskTool to update an existing task's properties.
Update Workspace LogoTool to upload an image as the workspace logo or delete the existing logo.
Upload FileTool to upload a file to Todoist.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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 you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Todoist account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Todoist

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Todoist access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called todoist_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["todoist"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Todoist actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Todoist actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, todoist)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Todoist tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Todoist database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Todoist

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Todoist, then start asking questions.

Complete Code

Here's the complete code to get you started with Todoist and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["todoist"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Todoist actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Todoist actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Todoist to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Todoist tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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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 LlamaIndex?

Yes, you can. LlamaIndex 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.

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