A persistent todo list server that enables AI assistants to manage tasks across different platforms using the Model Context Protocol. It provides tools for creating, listing, updating, and deleting todos with support for priorities, tags, and due dates.
A robust Model Context Protocol server for managing todos with capabilities for task creation, filtering, and statistical analysis. It enables AI assistants to interact with todo datasets through specialized tools, structured resources, and intelligent productivity prompts.
A server implementation that enables LLMs to programmatically manage tasks in Todo.txt files using the Model Context Protocol (MCP), supporting operations like adding, completing, deleting, listing, searching, and filtering tasks.
A demo MCP server exposing an in-memory todo list via tools (add_task, list_tasks, complete_task), a resource, and a prompt, over both stdio and HTTP transports.
A Model Context Protocol (MCP) server that provides tools for managing todo items, including creation, updating, completion, deletion, searching, and summarizing tasks.
