Summary This project offers a local-first, zero-setup TypeScript server for document management and AI-powered semantic search. It bridges knowledge gaps by integrating Google Gemini for advanced analysis and contextual understanding, alongside traditional embedding-based search. Aimed at developers and technical users, it provides a performant solution for organizing and querying information within frameworks, APIs, or internal guides.

How It Works The MCP Documentation Server acts as a local backend, persisting documents and embeddings on disk with an in-memory index for fast retrieval. It leverages Google Gemini AI for sophisticated natural language queries, summarization, and contextual insights, complementing its core semantic search via chunking and embeddings. Performance is optimized through O(1) lookups, LRU embedding cache, parallel processing for ingestion, and streaming file reads for large documents.

Quick Start & Requirements

• Primary Install/Run: Execute via npx -y @andrea9293/mcp-documentation-server within an MCP client configuration.
• Prerequisites: Google Gemini API Key (optional, for AI search). Node.js/npm required for npx. Embedding models download on first use.
• Links: None explicitly provided in the README.

Highlighted Details

• AI-Powered Search: Integrates Google Gemini for advanced document analysis, contextual understanding, and natural language querying.
• Performance Optimizations: Features O(1) lookups, LRU embedding caching, parallel chunking, and streaming file reads for efficient large-document processing.
• Local-First Architecture: Utilizes on-disk persistence and an in-memory index, requiring no external database.
• Flexible Embedding Models: Supports configurable embedding models, including multilingual options, with automatic downloads.

Maintenance & Community Follows standard GitHub contribution flow (fork, branch, PR) with Conventional Commits. A "MCP Community" is mentioned, but specific links are absent.

Licensing & Compatibility Released under the MIT license, permitting commercial use and integration into closed-source applications.

Limitations & Caveats AI search requires a Google Gemini API key. Changing embedding models necessitates re-processing all documents due to incompatible embedding dimensions.