Todoist AI MCP Server Overview

The Todoist AI MCP server bridges the gap between natural‑language AI assistants and the productivity platform Todoist. By exposing a set of well‑structured tools—such as searching for tasks, adding new items, and fetching detailed task information—the server lets AI agents read from and write to a user’s Todoist account without exposing raw API credentials. This removes the friction of manual OAuth flows and enables developers to embed task‑management capabilities directly into conversational workflows, whether they’re building chatbots, virtual assistants, or integrated IDE helpers.

At its core, the server implements the Model Context Protocol (MCP), a lightweight HTTP service that standardizes how tools are described, invoked, and returned. The Todoist MCP server bundles a small collection of high‑level tools that represent common task‑management workflows: findTasksByDate, addTasks, search, and fetch. These tools are designed to be reusable; developers can import them into any MCP‑compatible client or use the server as a drop‑in endpoint. The result is an AI that can, for example, ask “What’s on my schedule tomorrow?” and receive a neatly formatted list of tasks, or add a new reminder with a single prompt.

Key capabilities include:

• OAuth‑secured access – Users authenticate once via the server’s built‑in wizard, after which the assistant can act on their behalf.
• Atomic and composite actions – While individual tools perform single operations, the server’s design encourages chaining them into full workflows (e.g., searching for a task and then updating its status).
• OpenAI MCP compatibility – The search and fetch tools adhere to OpenAI’s MCP spec, ensuring seamless integration with any OpenAI‑based client.
• Extensibility – The tools are written in plain TypeScript and can be extended or replaced, allowing teams to add custom logic (e.g., priority tagging) without modifying the server.

Real‑world use cases span personal productivity, team collaboration, and developer tooling. A project manager could ask an AI assistant to “Add a follow‑up email task for the client meeting next week” and receive confirmation instantly. In software development, a VS Code extension could let developers create GitHub‑linked Todoist tasks by simply describing the issue in chat. For everyday users, a voice assistant could read out tomorrow’s tasks or add new items on the fly.

Integrating the Todoist MCP server into an AI workflow is straightforward: any MCP‑compatible client (Claude Desktop, Cursor, VS Code, or custom LLM pipelines) can point to the server’s HTTP endpoint and receive a catalog of tools. Once authenticated, the assistant can invoke these tools via natural language prompts, and the server returns structured JSON responses that the LLM can parse and present. This tight coupling of language understanding with task‑management operations delivers a fluid, contextually aware user experience that scales from simple to complex productivity scenarios.