# 1. Goals and Background Context
## Goals
* To establish the server as the recognized, community-supported standard for integrating AI models with LunaTask.
* To provide a stable, well-documented, and easy-to-deploy server that simplifies a complex integration challenge.
* To enable seamless integration with primary target platforms like Home Assistant and local, private LLMs.
* To achieve full API parity with the create/update capabilities of the LunaTask REST API over time.
* To deliver a high-quality, stable solution, measured by the percentage of code covered by automated tests.
## Background Context
The core problem this project addresses is the significant technical barrier for developers and advanced users wishing to connect modern AI tools, particularly local and private LLMs, with their data in the security-focused LunaTask application. The current need to write custom, single-purpose API clients is inefficient and hinders innovation in the personal productivity space.
This PRD outlines the requirements for an open-source Model Context Protocol (MCP) server that will act as a standardized, privacy-respecting bridge to the LunaTask API. It is designed to be lightweight, cross-platform, and easy to deploy, with a primary focus on serving the needs of the "Privacy-Conscious Automator" archetype identified in the Project Brief. The initial MVP will focus on delivering core functionality for the most common use cases (Tasks and Habits), with a clear path to full API coverage in subsequent phases.
