Overview

The Mcp Godo server is a lightweight, Go‑implemented MCP endpoint that exposes a small but complete set of tools for managing a todo list. It addresses the common developer need to experiment with tool integration in AI assistants without the overhead of a full‑blown application. By storing todos in an SQLite database, it keeps persistence simple while still demonstrating how a real backend can be hooked into an AI workflow.

At its core, the server provides nine tools that cover the full CRUD lifecycle of a task: adding, completing, uncompleting, listing, retrieving, deleting, and updating due dates. Each tool is exposed through the MCP interface with clear parameter definitions, making it trivial for an AI assistant to call them directly from a prompt. The use of ISO 8601 dates and integer IDs keeps the API language‑agnostic, allowing any client that understands MCP to interact seamlessly.

For developers building AI workflows, this server offers a concrete example of how to turn everyday data operations into callable actions. A Claude assistant, for instance, can ask a user to “add a new todo” and then invoke the tool with the supplied title and optional due date. The assistant can then list active items via , or mark a task complete with . Because the tools are stateless and return JSON, chaining them in a conversation is straightforward, enabling complex task‑management flows without custom code.

Real‑world scenarios include personal productivity bots that keep a shared todo list in sync across devices, or project management assistants that pull tasks from a database and surface them during meetings. The server’s simple schema also makes it easy to extend: adding priority levels, tags, or a completion timestamp can be done with minimal schema changes and additional tools. Its Go implementation ensures low memory usage and fast startup, which is ideal for deployment in serverless or containerised environments.

Overall, Mcp Godo showcases how a minimal MCP server can provide tangible value to developers. It demonstrates clean tool definitions, reliable persistence with SQLite, and a ready‑to‑use set of actions that fit naturally into AI assistant workflows. By serving as both a learning resource and a practical utility, it bridges the gap between theoretical MCP concepts and real‑world application development.