MCP Documentation Server added to readme #2121
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Description
A TypeScript-based MCP server that provides document management and semantic search capabilities. Upload documents, search them with AI embeddings
Features:
📄 Document Management - Add, list, retrieve, and delete documents with metadata
🔍 Semantic Search - AI-powered search using embeddings
📁 File Upload - Drop .txt/.md/.pdf files in uploads folder for processing
🧩 Smart Chunking - Automatic text splitting for better search accuracy
🗑️ Document Deletion - Clean removal of documents and their chunks
🌍 Multilingual - Supports multiple languages with quality embeddings
💾 Local Storage - All data stored locally in ~/.mcp-documentation-server/ directory
⚡ Fast Setup - No database required, works out of the box
Server Details
Motivation and Context
How many times have you asked cursor or github copilot about new libraries, frameworks, or recent updates only to get outdated or incomplete answers?
My Solution: An MCP server that lets you "feed" fresh, up-to-date documentation directly to your AI through
How Has This Been Tested?
I am a Salesforce architect and all AIs are having trouble with LWC implementation because of syntax, metadata files and more on Salesforce platform. I uploaded Salesforce developer documentation and, I am not hallucinating anymore, the AI can understand the code better and write it.
I also uploaded MCP documentation with full-llms.txt and this was a great help for testing and implementing this same server.
some of my colleagues are also using it and they are finding it very good. tested in vs code and claude desktop
Breaking Changes
classic installation, no changes needed
Types of changes
Checklist
Additional context
Set via MCP_EMBEDDING_MODEL environment variable:
Xenova/all-MiniLM-L6-v2 (default) - Fast, good quality
Xenova/paraphrase-multilingual-mpnet-base-v2 (recommended) - Best quality, multilingual but more RAM usage
Important: Changing models requires re-adding all documents as embeddings are incompatible.