MCP‑Todoist Integration

The MCP‑Todoist server turns the popular task‑management service Todoist into a first‑class data source for AI assistants that speak the Model Context Protocol. It solves the common pain point of having a language model understand, create, and update real‑world tasks without leaving the conversational interface. Developers can embed Todoist’s full API—tasks, projects, sections, labels, comments and collaborator data—directly into Claude or any other MCP‑compatible client. This eliminates the need for manual API calls, custom wrappers, or browser interactions.

By exposing Todoist’s REST endpoints as MCP tools, the server allows an assistant to perform CRUD operations on tasks and projects in a single prompt. For example, a user can ask the model to “add a new task titled Finish quarterly report due tomorrow” and the assistant will translate that natural‑language request into a tool call, send it to Todoist, and return the created task’s ID. The same flow works for updating priorities, moving tasks between projects, or deleting completed items. The server also supports filtering queries—by label, project, due date, or assignee—so the assistant can surface relevant tasks without exposing raw API calls to the user.

Key capabilities include:

• Full CRUD support for tasks, projects, sections, labels, and comments.
• Collaborator visibility, allowing the assistant to list or invite teammates.
• Rich filtering across multiple criteria (due dates, labels, project membership).
• Well‑documented tool signatures that map directly to the Todoist API, making it straightforward for developers to understand and extend.

Real‑world use cases span personal productivity assistants that keep a user’s Todoist in sync with their day, team collaboration bots that auto‑assign tasks based on project health, and workflow automation scripts that trigger Todoist updates from other services (e.g., calendar events or email). In an AI‑driven development pipeline, a model could read pending tasks, suggest prioritization, or even draft task descriptions from meeting notes—all without leaving the chat.

Integrating MCP‑Todoist into an AI workflow is simple: a client declares the server in its MCP configuration, supplies the API token via environment variables, and then calls the exposed tools. The server handles authentication, rate limiting, and error translation, so developers can focus on higher‑level logic. Because the server is written in Python and follows MCP best practices, it can be run locally or deployed as a microservice behind any cloud provider, giving teams flexibility and control over data residency.

In summary, MCP‑Todoist bridges the gap between conversational AI and real‑world task management. It empowers developers to build intelligent assistants that can read, create, update, and delete Todoist items seamlessly, turning natural language into productive actions.