Todoist MCP Server

The Todoist MCP Server bridges the gap between a popular task‑management platform and AI assistants that understand the Model Context Protocol. By exposing Todoist’s REST API as a set of MCP tools, developers can give Claude or other LLMs the ability to read, create, update, and delete tasks directly from natural‑language prompts. This eliminates the need for manual API calls or custom scripts, allowing conversational agents to become real productivity assistants that work within the user’s existing Todoist workflow.

At its core, the server offers a comprehensive collection of tools that mirror the key operations available in Todoist. Projects can be listed, retrieved, created, updated, or removed; sections within a project can be queried; and tasks can be fetched with filters such as “last 7 days completed.” Each tool follows the MCP naming convention () and accepts structured arguments, making it trivial for an AI to construct a request that matches the user’s intent. Because the server runs locally (or in a Docker container), sensitive data such as the API token never leaves the user’s machine, giving developers peace of mind about privacy and security.

Developers who build AI‑driven workflows benefit from the server’s tight integration with existing MCP clients like Claude Desktop and Goose. In a typical scenario, a user opens a conversation, asks “What tasks are due tomorrow?” and the assistant invokes with a filter for the next day. The server responds instantly, and the assistant can then propose scheduling or re‑prioritizing actions. This pattern scales to complex use cases: generating a weekly report, moving items between projects after completion, or automatically archiving old sections—all through natural language commands.

The server’s design also offers unique advantages. It is written in Python, making it straightforward to extend or customize for niche workflows; developers can add new tools by simply defining additional functions in the file. The use of UV as a package manager ensures fast, reproducible deployments across platforms, while the ability to run from a local copy or a Git URL gives flexibility for continuous integration pipelines. Finally, the server’s compatibility with both cloud‑hosted and local LLMs (via Goose or other clients) means teams can choose the deployment model that best fits their latency, cost, and data‑control requirements.

In short, the Todoist MCP Server turns a powerful task‑management API into an AI‑friendly service. It empowers developers to build conversational assistants that can read and manipulate real work items without leaving the chat, thereby transforming how teams plan, track, and reflect on their productivity.