MCP Documentation Server

What is MCP Documentation Server?

MCP Documentation Server is a Model Context Protocol (MCP) server that enables local document management and semantic search. It allows users to upload documents, search them using AI embeddings, and integrate seamlessly with MCP clients like Claude Desktop and VS Code. It is designed to help manage and access knowledge efficiently, especially for new frameworks, API documentation, and internal guides.

How to use MCP Documentation Server?

To use the MCP Documentation Server, you can follow these steps:

1. Install and Run: The recommended way is to run directly with npx @andrea9293/mcp-documentation-server.
2. Configure MCP Client: Add the server to your MCP client configuration (e.g., Claude Desktop) by specifying the command and environment variables for the embedding model.
3. Start Using: Once configured, you can add documents (text/markdown files or direct content), search them using semantic search, and manage your content (list, retrieve, organize).

Usage Examples:

• Adding a Document: Use the add_document tool with title, content, and optional metadata.
• Searching Documents: Use the search_documents tool with a document ID, query, and limit.
• Deleting a Document: Use the delete_document tool with the document ID.
• File Upload Workflow: Get the uploads path, place .txt, .md, or .pdf files in the folder, and then process them using process_uploads.

Key Features of MCP Documentation Server

• Document Management: Add, list, retrieve, and delete documents with associated metadata.
• Semantic Search: AI-powered search utilizing embeddings for accurate results.
• File Upload: Supports uploading .txt, .md, and .pdf files for processing.
• Smart Chunking: Automatic text splitting to enhance search accuracy.
• Document Deletion: Clean removal of documents and their associated chunks.
• Multilingual Support: Compatible with multiple languages through quality embeddings.
• Local Storage: All data is stored locally in ~/.mcp-documentation-server/ without requiring a separate database.
• Fast Setup: Zero-setup requirement, works out of the box.

Use Cases of MCP Documentation Server

• New Frameworks: Efficiently manage and search documentation for new software frameworks.
• API Documentation: Organize and provide semantic search for API specifications.
• Internal Guides: Create and manage internal knowledge bases and guides for teams.
• Personal Knowledge Base: Build a searchable repository of personal notes, articles, and research.

FAQ from MCP Documentation Server

Q: Why does it take time on first use? A: The embedding models download on first use, which can be around 420MB for the best model. Please wait for the background download to complete.

Q: Why are my search results inconsistent or inaccurate? A: This can happen if you have mixed embedding models within the same dataset. It is recommended to stick to one model or re-add all documents after switching models to ensure compatibility and accuracy.

MCP Documentation Server

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.

Demo Video

Quick Start

1. Install and Run

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

2. Configure MCP Client

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"
      }
    }
  }
}

3. Start Using

• 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

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

Available Tools

Tool	Description
add_document	Add a document with title, content, and metadata
search_documents	Search for chunks within a specific document
list_documents	List all documents with their metadata
get_document	Retrieve a complete document by ID
delete_document	Delete a document by ID (removes all associated chunks)
get_uploads_path	Get path to uploads folder
list_uploads_files	List files in uploads folder
process_uploads	Process uploaded files into documents

Usage Examples

Adding a Document

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

Searching Documents

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

Deleting a Document

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

File Upload Workflow

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)

Configuration

Data Storage

All documents and uploads are stored locally in:

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

Embedding Models

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.

Installation Options

NPX (Recommended)

npx @andrea9293/mcp-documentation-server

Global Installation

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

From Source

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

Best Practices

Document Organization

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

Search Optimization

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

Performance Tips

• 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

Troubleshooting

Timeout on First Use

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

Search Results Issues

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

Development

# Development server with hot reload
npm run dev

# Build and test
npm run build

# Inspect tools with web UI
npm run inspect

Contributing

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

License

MIT - see LICENSE file

Support

• 📖 Documentation
• 🐛 Report Issues
• 💬 MCP Community

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