S3 Documentation MCP Server

A lightweight Model Context Protocol (MCP) server that brings RAG (Retrieval-Augmented Generation) capabilities to your LLM over Markdown documentation stored on S3.

Built for simplicity:

• 🪶 Lightweight Stack: No heavy dependencies or cloud services
• 🏠 Flexible Embeddings: Choose between Ollama (local, free) or OpenAI (cloud, high-accuracy)
• 💾 File-based Storage: Vector indices stored as simple files (HNSWLib)
• 🔌 S3-Compatible: Works with any S3-compatible storage (AWS, MinIO, Scaleway, Cloudflare R2...)

Important

🚧 This project is a work in progress. APIs and behavior may change at any time, and backward compatibility is not ensured. Not suitable for production.

Requirements

• Embedding Provider (choose one):
  • Ollama (recommended for local/offline use) with the nomic-embed-text model
  • OpenAI API Key (for cloud-based embeddings)
• Node.js >= 18 (if running from source) OR Docker (recommended)
• S3-compatible storage (AWS S3, MinIO, Scaleway, Cloudflare R2, etc.)

Use Cases

• 📚 Product Documentation: Let Claude/Cursor/etc answer from your docs
• 🏢 Internal Wiki: AI-powered company knowledge search
• 📖 API Docs: Help developers find API information
• 🎓 Educational Content: Build AI tutors with course materials

Quick Start

With Docker (Recommended)

# 1. Prerequisites
# Install Ollama from https://ollama.ai
ollama pull nomic-embed-text

# 2. Configure
cp env.example .env  # Add your S3 credentials

# 3. Run
docker run -d \
  --name s3-doc-mcp \
  -p 3000:3000 \
  --env-file .env \
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  -v $(pwd)/data:/app/data \
  yoanbernabeu/s3-doc-mcp:latest

Or use Docker Compose (Local Build):

docker compose up -d

From Source

# 1. Prerequisites
# Install Ollama from https://ollama.ai
ollama pull nomic-embed-text

# 2. Install & Run
npm install
cp env.example .env  # Configure your S3 credentials
npm run build && npm start

# 3. For local development
npm run dev

Your MCP server is now running on http://localhost:3000

Connect to MCP Clients

Once your server is running, you need to configure your MCP client to connect to it.

Cursor

Edit your ~/.cursor/mcp.json file and add:

{
  "mcpServers": {
    "doc": {
        "type": "streamable-http",
        "url": "http://127.0.0.1:3000/mcp",
        "note": "S3 Documentation RAG Server"
    }
  }
}

Claude Desktop

Edit your Claude Desktop configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "doc": {
        "type": "streamable-http",
        "url": "http://127.0.0.1:3000/mcp",
        "note": "S3 Documentation RAG Server"
    }
  }
}

Restart your MCP client, and you should now see:

• 3 MCP Tools: search_documentation, refresh_index, get_full_document
• MCP Resources: Full list of indexed documentation files with direct access

💡 Tip: If using Docker, make sure the port mapping matches your configuration (default is 3000:3000)

Features

• 🔌 Universal S3: AWS S3, MinIO, Scaleway, DigitalOcean Spaces, Cloudflare R2, Wasabi...
• 🧠 Flexible Embeddings:
  • Ollama (nomic-embed-text) - Local, free, offline-capable
  • OpenAI (text-embedding-3-small, text-embedding-3-large) - Cloud-based, high-accuracy, multilingual
• 🔄 Smart Sync: Incremental updates via ETag comparison + automatic full sync on empty vector store
• ⚡ Fast Search: HNSWLib vector index with cosine similarity
• 🔐 Optional Auth: API key authentication for secure deployments
• 🛠️ 3 MCP Tools: search_documentation, refresh_index, and get_full_document
• 📚 MCP Resources: Native support for discovering and reading indexed files via standard MCP Resources API

How It Works

The server follows a simple pipeline:

1. S3Loader: Scans your S3 bucket for .md files, downloads their content, and tracks ETags for change detection
2. SyncService: Detects new, modified, or deleted files and performs incremental synchronization (no unnecessary reprocessing)
3. VectorStore:
   • Splits documents into chunks (1000 characters by default)
   • Generates embeddings using your chosen provider:
     • Ollama: nomic-embed-text (local, free)
     • OpenAI: text-embedding-3-small or text-embedding-3-large (cloud, high-accuracy)
   • Indexes vectors using HNSWLib for fast similarity search
4. MCP Server: Exposes both Tools and Resources via HTTP:
   • Tools: search_documentation, refresh_index, get_full_document for semantic search and actions
   • Resources: resources/list, resources/read for file discovery and direct access

What is HNSWLib?

HNSWLib (Hierarchical Navigable Small World) is a lightweight, in-memory vector search library that's perfect for this use case:

• ⚡ Fast: Approximate nearest neighbor search in milliseconds
• 💾 Simple: Stores indices as local files (no database needed)
• 🪶 Efficient: Low memory footprint, ideal for personal/small-team documentation
• 🎯 Accurate: High recall with cosine similarity for semantic search

It's the sweet spot between simplicity and performance for RAG applications.

Configuration

Copy env.example to .env and configure your environment variables:

cp env.example .env

Essential Variables

# S3 Configuration
S3_BUCKET_NAME=your-bucket-name           # Your S3 bucket name
S3_ACCESS_KEY_ID=your-access-key          # S3 access key
S3_SECRET_ACCESS_KEY=your-secret-key      # S3 secret key
S3_REGION=us-east-1                       # S3 region
S3_ENDPOINT=                              # Optional: for non-AWS S3 (MinIO, Scaleway, etc.)

# Embeddings Provider (choose one)
EMBEDDING_PROVIDER=ollama                 # ollama (default) or openai

# Option 1: Ollama (Local)
OLLAMA_BASE_URL=http://localhost:11434    # Ollama API endpoint
OLLAMA_EMBEDDING_MODEL=nomic-embed-text   # Ollama embedding model

# Option 2: OpenAI (Cloud) - Only if EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=                           # Your OpenAI API key
OPENAI_EMBEDDING_MODEL=text-embedding-3-small  # or text-embedding-3-large

See env.example for all available options and detailed documentation (RAG parameters, sync mode, chunk size, etc.).

Embedding Providers

The server supports two embedding providers:

🏠 Ollama (Local) - Default

Pros:

• ✅ Free: No API costs, unlimited usage
• ✅ Private: All data stays on your machine
• ✅ Offline: Works without internet connection
• ✅ Fast: Direct local API calls

Cons:

• ⚠️ Requires Ollama installation and model download
• ⚠️ Uses local CPU/GPU resources

Setup:

# Install Ollama from https://ollama.ai
ollama pull nomic-embed-text

# Configure
EMBEDDING_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_EMBEDDING_MODEL=nomic-embed-text

☁️ OpenAI (Cloud)

Pros:

• ✅ High Accuracy: State-of-the-art embeddings
• ✅ Multilingual: Excellent support for 20+ languages
• ✅ No Local Resources: Runs entirely in the cloud
• ✅ Lower Latency: Fast API responses

Cons:

• ⚠️ Requires API key and credits
• ⚠️ Data sent to OpenAI servers
• ⚠️ Cost per token (very affordable: ~$0.00002/1K tokens for text-embedding-3-small)

Setup:

# Get an API key from https://platform.openai.com/api-keys

# Configure
EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=sk-...your-key...
OPENAI_EMBEDDING_MODEL=text-embedding-3-small  # or text-embedding-3-large

Model Comparison:

Model	Dimensions	Performance	Cost	Best For
text-embedding-3-small	1536	High	Low	General purpose, cost-sensitive
text-embedding-3-large	3072	Higher	Medium	Maximum accuracy, multilingual

💡 Tip: Start with text-embedding-3-small for most use cases. Only switch to text-embedding-3-large if you need the absolute best accuracy or work extensively with non-English content.

Fallback Behavior:

If you set EMBEDDING_PROVIDER=openai but don't provide a valid OPENAI_API_KEY, the server will automatically fall back to Ollama (if configured). This ensures the server can always start, even with incomplete configuration.

Synchronization Modes

The server supports three synchronization modes via SYNC_MODE:

• startup (default): Syncs at server startup

  • ✅ Auto-detection: If the vector store is empty, automatically performs a full sync
  • ✅ Otherwise, performs an incremental sync (only changed files)
  • ✅ No manual refresh_index needed after restart!

• periodic: Syncs at regular intervals (SYNC_INTERVAL_MINUTES)

  • Runs incremental syncs automatically

• manual: No automatic sync

  • You must call refresh_index tool manually

💡 Note: The server automatically detects when the vector store is empty (e.g., after deleting ./data/ folder or first run) and triggers a full synchronization. You no longer need to manually run refresh_index after every restart!

🔐 Security & Authentication

API Key Authentication (Optional)

By default, the server runs in open access mode for easy local development. For shared or remote deployments, you can enable API key authentication:

# Enable authentication
ENABLE_AUTH=true

# Set your API key
MCP_API_KEY=your-secret-key-here

When authentication is enabled:

• ✅ All endpoints (except /health) require a valid API key
• ✅ API key can be provided via:
  • Authorization header (recommended): Authorization: Bearer your-secret-key
  • Query parameter: ?api_key=your-secret-key
• ✅ Invalid or missing keys return HTTP 401 Unauthorized

Usage Examples:

# With Authorization header (recommended)
curl -H "Authorization: Bearer your-secret-key" http://localhost:3000/mcp

# With query parameter
curl "http://localhost:3000/mcp?api_key=your-secret-key"

MCP Client Configuration with API Key:

{
  "mcpServers": {
    "doc": {
      "type": "streamable-http",
      "url": "http://127.0.0.1:3000/mcp",
      "headers": {
        "Authorization": "Bearer your-secret-key"
      },
      "note": "S3 Documentation RAG Server with authentication"
    }
  }
}

💡 Best Practices:

• Keep authentication disabled for local development
• Enable it for shared networks or remote deployments
• Use strong, randomly generated keys (e.g., openssl rand -hex 32)
• The /health endpoint is always accessible without authentication for monitoring

MCP Tools

search_documentation

{
  "query": "How to configure S3?",
  "max_results": 4
}

Returns relevant document chunks with similarity scores and sources.

refresh_index

{
  "force": false  // default: incremental sync (recommended)
}

Synchronizes the documentation index with S3, detecting new, modified, or deleted files.

Parameters:

• force (boolean, optional, default: false)
  • false: Incremental sync - Only processes changes (fast, efficient) ✅
  • true: Full reindex - Reprocesses ALL files (slow, expensive) ⚠️

⚠️ Important: The force parameter should ONLY be set to true when explicitly needed (e.g., "force reindex", "rebuild everything from scratch"). Full reindex is expensive:

• Re-downloads all files from S3
• Regenerates all embeddings
• Rebuilds the entire vector store

For normal operations, always use incremental sync (default behavior).

get_full_document

{
  "s3_key": "docs/authentification_magique_symfony.md"
}

Retrieves the complete content of a Markdown file from S3 along with metadata:

• Full S3 key: The document's S3 identifier
• Complete Markdown content: Entire document (not chunked)
• Metadata: Size in bytes, last modification date, ETag, chunk count (if indexed)

Use Cases:

• View the complete document after finding it via search_documentation
• Export documentation for external use
• Understand the full context around a search result
• Display complete documents in third-party integrations

Important Notes:

• If a document appears in search results but get_full_document returns "not found", it means the file was deleted from S3 after being indexed
• Solution: Run refresh_index to synchronize the index with the current S3 state
• The tool will provide a helpful error message indicating when a sync is needed

MCP Resources

In addition to the 3 tools, the server implements MCP Resources for file discovery and direct access:

• resources/list: Lists all indexed Markdown files with metadata (name, URI, size, chunks, last modified)
• resources/read: Reads the full content of a specific file by its URI (e.g., s3doc://docs/authentication.md)

Use case: When users ask "What files do you have?" or "Show me file X", the LLM can browse and access files directly without semantic search.

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details on how to submit pull requests, report issues, and contribute to the project.

📝 License

MIT

👤 Author

Yoan Bernabeu