Todoist MCP Server Extended

The Todoist MCP Server Extended bridges the gap between natural‑language AI assistants and the Todoist task‑management ecosystem. By exposing a rich set of MCP tools that mirror core Todoist operations, the server allows Claude (or any compatible LLM) to create, update, and organize tasks without leaving the conversational interface. This solves a common pain point for developers who rely on AI to prototype workflows or build productivity apps: the need to manually switch between a task manager and an LLM. With the server in place, a single prompt can trigger complex Todoist actions, making AI‑driven task automation seamless and intuitive.

At its heart, the server offers task management primitives—create, update, complete, and delete tasks—alongside label, project, and section manipulation. These capabilities are wrapped in MCP tools that accept natural‑language arguments, such as “Create task ‘Review PR’ in project ‘Work’ section ‘To Do’.” The design prioritizes LLM efficiency: tools support batch operations and custom parameters, reducing the number of round‑trips required for large updates. Smart search and flexible filtering further empower developers to retrieve tasks by partial names, due dates, priorities, or labels, enabling sophisticated querying directly from the assistant.

Real‑world scenarios abound. A developer can ask Claude to “Show all tasks with label ‘Important’ in project ‘Work’,” and the assistant will query Todoist, filter by priority, and return a concise list—all within the chat. In an agile setting, a product owner might instruct Claude to “Move task ‘Documentation’ to section ‘In Progress’” and have the change reflected instantly in Todoist, keeping the team’s board up to date without manual intervention. For batch processing, a user can request “Mark all overdue tasks as high priority,” and the server will execute a bulk update efficiently.

Integration into AI workflows is straightforward: once the MCP server is registered in the client configuration, its tools become available as callable actions. Developers can embed these calls inside prompts or chain them with other MCP tools, enabling complex multi‑step processes—such as automatically generating a sprint backlog from user stories or scheduling recurring maintenance tasks. The server’s emphasis on batch operations and custom parameters gives it an edge over simpler adapters, allowing fine‑grained control that scales with larger task volumes.

In summary, the Todoist MCP Server Extended transforms a standard todo list into an AI‑ready API. It empowers developers to harness natural language for precise task manipulation, reduces context switching, and unlocks powerful automation possibilities within existing AI‑centric workflows.