Todo List MCP Server

The Todo List MCP server is a lightweight, purpose‑built backend that exposes a full CRUD API for managing personal or team task lists through the Model Context Protocol. By translating everyday todo operations into MCP tools, it lets AI assistants such as Claude act as a natural‑language interface to a persistent task database. Developers can therefore give voice or text commands like “Create a todo for tomorrow’s meeting” and have the assistant automatically persist that item, retrieve it later, or summarize all pending tasks—all without writing any custom code for each operation.

At its core the server offers a set of ten tools that cover every common workflow: creating, reading, updating, completing, and deleting items; searching by title or creation date; listing active tasks; and generating a concise summary of pending work. Each tool follows the MCP specification for inputs, outputs, and error handling, making it trivial to chain calls or embed them in larger AI‑driven processes. The server’s design encourages modularity: the data model, business logic, and formatting helpers are isolated in separate directories, so contributors can extend or replace any part—such as swapping a JSON file for a real database—without touching the MCP contract.

Real‑world use cases include personal productivity assistants that keep a shared task list in sync across devices, project management bots that surface overdue items during stand‑up meetings, or customer support agents that pull open tickets from a central todo store to offer status updates. Because the tools are exposed over MCP, any AI platform that understands the protocol can integrate this server with minimal effort—whether it’s a desktop application, a web chat bot, or an embedded device.

The server’s standout advantage is its educational value. The accompanying guide walks developers through the rationale behind each design choice, while the heavily commented source demonstrates how to implement MCP tools from scratch. This makes it an ideal starting point for teams looking to prototype their own domain‑specific MCP servers or teach newcomers how AI assistants can be wired to external services.