Qdrant MCP Server

A Model Context Protocol (MCP) server providing semantic search capabilities using Qdrant vector database with multiple embedding providers.

Features

• Zero Setup: Works out of the box with Ollama - no API keys required
• Privacy-First: Local embeddings and vector storage - data never leaves your machine
• Code Vectorization: Intelligent codebase indexing with AST-aware chunking and semantic code search
• Git History Search: Index commit history for semantic search over past changes, fixes, and patterns
• Advanced Search: Contextual search (code + git with correlations) and federated search across multiple repositories
• Multiple Providers: Ollama (default), OpenAI, Cohere, and Voyage AI
• Hybrid Search: Combine semantic and keyword search for better results
• Semantic Search: Natural language search with metadata filtering
• Incremental Indexing: Efficient updates - only re-index changed files
• Configurable Prompts: Create custom prompts for guided workflows without code changes
• Rate Limiting: Intelligent throttling with exponential backoff
• Full CRUD: Create, search, and manage collections and documents
• Structured Logging: JSON logging via Pino with configurable log levels
• Flexible Deployment: Run locally (stdio) or as a remote HTTP server
• API Key Authentication: Connect to secured Qdrant instances (Qdrant Cloud, self-hosted with API keys)

Quick Start

Prerequisites

• Node.js 22.x or 24.x
• Podman or Docker with Compose support

Installation

# Clone and install
git clone https://github.com/mhalder/qdrant-mcp-server.git
cd qdrant-mcp-server

# Node 22.x
npm install

# Node 24.x (requires C++20 flag for native module compilation)
CXXFLAGS='-std=c++20' npm install

# Start services (choose one)
podman compose up -d   # Using Podman
docker compose up -d   # Using Docker

# Pull the embedding model
podman exec ollama ollama pull nomic-embed-text  # Podman
docker exec ollama ollama pull nomic-embed-text  # Docker

# Build
npm run build

Configuration

Local Setup (stdio transport)

claude mcp add --transport stdio qdrant -- node /path/to/qdrant-mcp-server/build/index.js

Or add to ~/.claude.json:

{
  "mcpServers": {
    "qdrant": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/qdrant-mcp-server/build/index.js"]
    }
  }
}

For Qdrant Cloud or secured instances, add --env QDRANT_API_KEY=your-key or set in env config.

Try it:

Create a collection called "notes" and add a document about machine learning

Enable example prompts: Copy prompts.example.json to prompts.json and restart. Use /prompt to list available prompts.

Remote Setup (HTTP transport)

⚠️ Security Warning: When deploying the HTTP transport in production:

• Always run behind a reverse proxy (nginx, Caddy) with HTTPS
• Implement authentication/authorization at the proxy level
• Use firewalls to restrict access to trusted networks
• Never expose directly to the public internet without protection
• Consider implementing rate limiting at the proxy level
• Monitor server logs for suspicious activity

Start the server:

TRANSPORT_MODE=http HTTP_PORT=3000 node build/index.js

Option 1: Using claude mcp add

claude mcp add --transport http qdrant http://your-server:3000/mcp

Option 2: Add to ~/.claude.json

{
  "mcpServers": {
    "qdrant": {
      "type": "http",
      "url": "http://your-server:3000/mcp"
    }
  }
}

Using a different provider:

"env": {
  "EMBEDDING_PROVIDER": "ope

... [View full README on GitHub](https://github.com/mhalder/qdrant-mcp-server#readme)