Lunatask
Provides a standardized bridge between AI models and the LunaTask API for task management.
About
Lunatask MCP is an unofficial Model Context Protocol (MCP) server designed to connect AI models with the LunaTask API. Built as a lightweight, asynchronous Python application using the FastMCP framework, it operates as a local subprocess, enabling AI tools to interact seamlessly with LunaTask data. It facilitates automated task creation, updates, deletion, and habit tracking, while also exposing LunaTask data as MCP resources for compatible clients.
Key Features
- Functions as an unofficial Model Context Protocol (MCP) server for LunaTask.
- Enables AI models to create, update, and delete tasks within LunaTask.
- Allows AI tools to track habit activity for specific habit IDs and dates.
- Exposes LunaTask data, including discovery, single tasks, and filtered area/global lists, as MCP resources.
- Offers flexible configuration via TOML files and command-line arguments, with robust validation and security features.
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Use Cases
- Integrating AI agents and large language models with LunaTask for automated task creation and management.
- Viewing and filtering LunaTask data programmatically through compatible MCP clients.
- Automating habit tracking and other productivity workflows in LunaTask using AI-driven tools.
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