How to Use the Todoist MCP in CrewAI

Q: How does Todoist interact with CrewAI for task listing?

An agent calls listtasks to get all active items, potentially filtered by a project ID. The resulting list is then passed to another specialized agent for review and analysis.

Q: Can I use Todoist status updates with CrewAI?

Yes. You can call completetask when the team determines an item is finalized, allowing the autonomous operation to move the task's status forward in Todoist.

Q: Does the Todoist MCP Server handle reading tasks with CrewAI?

It uses listtasks for reading active items. The agent can filter these results by project ID or simply get a full list of everything that needs attention.

Let specialized agents manage your Todoist list using CrewAI's multi-agent team collaboration model.

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Connect Todoist MCP to CrewAI

Create your Vinkius account to connect Todoist to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Todoist with CrewAI

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Analyze Comment History on Todoist Tasks

The `list_comments` tool lets a dedicated agent pull every comment for one specific task. This is crucial when you need an agent to research the background of an item before taking action. The specialized memory shared by agents allows them to pass this gathered context—the comments—to another agent for analysis.

Marking Tasks Complete in Todoist

An agent can use `complete_task` when the research phase is done. It's a simple, single action that changes the status of an item. The sequential execution model means one agent gathers confirmation (maybe via `list_tasks`), and another executes the final mark-as-done step.

Mapping Todoist Projects and Labels

To give agents context, they first run `list_projects` or `list_labels`. These tools provide the foundational organizational data. The monitor agent uses this list to determine which tasks are even worth looking at. This capability ensures that the entire autonomous operation stays within defined project boundaries.

Setup guide

Set up Todoist MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Todoist tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Todoist Analyst",
    goal="Access and analyze Todoist data via MCP.",
    backstory="Expert analyst with direct Todoist access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Todoist transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Todoist Analyst",
        goal="Access and analyze Todoist data via MCP.",
        backstory="Expert analyst with direct Todoist access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Todoist transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Todoist. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Todoist MCP in CrewAI

An agent calls `list_tasks` to get all active items, potentially filtered by a project ID. The resulting list is then passed to another specialized agent for review and analysis.

Yes. You can call `complete_task` when the team determines an item is finalized, allowing the autonomous operation to move the task's status forward in Todoist.

The system allows agents to collaborate: one agent gathers data using `list_sections`, and a second agent uses that structure to assign or modify tasks. The shared memory makes the handoff clean.

This server touches project IDs, section names, labels, task contents, and comment text. It manages structured organizational metadata related to user planning.

It uses `list_tasks` for reading active items. The agent can filter these results by project ID or simply get a full list of everything that needs attention.

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