A TypeScript-based Model Context Protocol (MCP) server that provides document management and semantic search capabilities. Upload documents, search them with AI embeddings, and integrate seamlessly with MCP clients like Claude Desktop.

# Run directly with npx (recommended)
npx @andrea9293/mcp-documentation-server

Add to your MCP client configuration (e.g., Claude Desktop):

{
  "mcpServers": {
    "documentation": {
      "command": "npx",
      "args": [
        "-y",
        "@andrea9293/mcp-documentation-server"
      ],
      "env": {
        "MCP_EMBEDDING_MODEL": "Xenova/all-MiniLM-L6-v2"
      }
    }
  }
}

• Add documents: Upload text/markdown files or add content directly
• Search documents: Use semantic search to find relevant information
• Manage content: List, retrieve, and organize your documents

• 📄 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

{
  "tool": "add_document",
  "arguments": {
    "title": "Python Basics",
    "content": "Python is a high-level programming language...",
    "metadata": {
      "category": "programming",
      "tags": ["python", "tutorial"]
    }
  }
}

{
  "tool": "search_documents",
  "arguments": {
    "document_id": "doc-123",
    "query": "variable assignment",
    "limit": 5
  }
}

{
  "tool": "delete_document",
  "arguments": {
    "id": "doc-123"
  }
}

1. Get uploads path: get_uploads_path (~/.mcp-documentation-server/uploads/)
2. Place your .txt/.md/.pdf files in that folder
3. Process files: process_uploads
4. Search the processed documents

Supported file types:

• .txt - Plain text files
• .md - Markdown files
• .pdf - PDF files (text extraction, no OCR)

All documents and uploads are stored locally in:

~/.mcp-documentation-server/
├── data/      # Document storage (JSON files)
└── uploads/   # Files to process (.txt, .md, .pdf)

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

⚠️ Important: Changing models requires re-adding all documents as embeddings are incompatible.

npx @andrea9293/mcp-documentation-server

npm install -g @andrea9293/mcp-documentation-server
mcp-documentation-server

git clone https://github.com/andrea9293/mcp-documentation-server.git
cd mcp-documentation-server
npm install
npm run build
npm start

• Use descriptive titles for easy identification
• Add relevant metadata (tags, categories) for better organization
• Keep documents focused on specific topics for better search accuracy

• Use specific, descriptive search queries
• Combine keywords related to your topic
• Start with broader queries, then refine with more specific terms

• Process large files during off-peak hours (initial embedding creation)
• Use smaller embedding models for faster performance if quality is acceptable
• Regularly clean up unused documents to maintain performance

• Cause: Embedding models download on first use (~420MB for best model)
• Solution: Wait for background download to complete, or use smaller model initially

• Cause: Mixed embedding models in same dataset
• Solution: Stick to one model or re-add all documents after switching

# Development server with hot reload
npm run dev

# Build and test
npm run build

# Inspect tools with web UI
npm run inspect

1. Fork the repository
2. Create feature branch: git checkout -b feature/name
3. Follow Conventional Commits for messages
4. Submit pull request

MIT - see LICENSE file

• 📖 Documentation
• 🐛 Report Issues
• 💬 MCP Community

Built with FastMCP and TypeScript 🚀