mhalder/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
bash
# 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)
bash
claude mcp add --transport stdio qdrant -- node /path/to/qdrant-mcp-server/build/index.js
Or add to ~/.claude.json:
json
"mcpServers": {
"qdrant": {
"type": "stdio",
"command": "node",
"args": ["/path/to/qdrant-mcp-server/build/index.js"]
For Qdrant Cloud or secured instances, add --env QDRANTAPIKEY=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:
bash
TRANSPORTMODE=http HTTPPORT=3000 node build/index.js
Option 1: Using claude mcp add
bash
claude mcp add --transport http qdrant http://your-server:3000/mcp
Option 2: Add to ~/.claude.json