Google Tasks MCP Server

The Google Tasks MCP Server bridges the gap between conversational AI assistants and Google’s task management ecosystem. By exposing a lightweight, TypeScript‑based MCP interface, it allows large language models (LLMs) and other AI agents to create, read, update, and delete tasks directly from Google Tasks without leaving the context of a dialogue. This eliminates the need for manual API calls or UI interactions, enabling more natural, voice‑oriented workflows where an assistant can “remember” to add a follow‑up or “check off” items on the fly.

At its core, the server defines a single resource namespace () that represents the default task list for an authenticated user. Each task is represented in a machine‑readable JSON format, exposing metadata such as title, notes, and completion status. The MCP tools—, , , , and —mirror the standard Google Tasks API operations but are wrapped in declarative tool definitions that LLMs can invoke with natural language intent. For example, an assistant could parse a user’s request “Add a reminder to call the client tomorrow” and automatically translate that into a call with appropriate parameters.

Developers benefit from the server’s structured tool definitions, which provide clear input schemas and predictable responses. This predictability simplifies prompt engineering and error handling in AI applications, as the assistant can rely on well‑defined outputs rather than parsing raw HTTP responses. Moreover, because tasks are exposed as resources with a uniform JSON MIME type, other components of an AI pipeline—such as state trackers or task planners—can consume the data seamlessly, enabling advanced features like automated scheduling, priority scoring, or cross‑platform synchronization.

Real‑world scenarios include virtual assistants that manage daily to‑do lists, project management bots that update task statuses based on meeting notes, or customer support agents that log follow‑up tasks from chat transcripts. By integrating this MCP server into an LLM’s toolset, developers can create end‑to‑end experiences where natural language commands translate directly into actionable changes in Google Tasks, all while maintaining the contextual integrity of the conversation.

Unique advantages of this implementation lie in its adherence to core MCP principles—resource‑centric design, JSON interoperability, and minimal runtime overhead—while offering a fully typed TypeScript codebase that eases maintenance. The inclusion of an MCP Inspector reference further streamlines debugging, allowing developers to visualize tool invocations and responses in real time. Overall, the Google Tasks MCP Server delivers a robust, developer‑friendly bridge between AI assistants and everyday productivity tools.