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Qdrant MCP Server

by steiner385
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid
MIT License

Qdrant MCP Server

A Model Context Protocol (MCP) server that provides semantic code search capabilities using Qdrant vector database and OpenAI embeddings.

Features

  • 🔍 Semantic Code Search - Find code by meaning, not just keywords

  • 🚀 Fast Indexing - Efficient incremental indexing of large codebases

  • 🤖 MCP Integration - Works seamlessly with Claude and other MCP clients

  • 📊 Background Monitoring - Automatic reindexing of changed files

  • 🎯 Smart Filtering - Respects .gitignore and custom patterns

  • 💾 Persistent Storage - Embeddings stored in Qdrant for fast retrieval

Related MCP server: Qdrant Retrieve MCP Server

Installation

Prerequisites

  • Node.js 18+

  • Python 3.8+

  • Docker (for Qdrant) or Qdrant Cloud account

  • OpenAI API key

Quick Start

# Install the package npm install -g @kindash/qdrant-mcp-server # Or with pip pip install qdrant-mcp-server # Set up environment variables export OPENAI_API_KEY="your-api-key" export QDRANT_URL="http://localhost:6333" # or your Qdrant Cloud URL export QDRANT_API_KEY="your-qdrant-api-key" # if using Qdrant Cloud # Start Qdrant (if using Docker) docker run -p 6333:6333 qdrant/qdrant # Index your codebase qdrant-indexer /path/to/your/code # Start the MCP server qdrant-mcp

Configuration

Environment Variables

Create a .env file in your project root:

# Required OPENAI_API_KEY=sk-... # Qdrant Configuration QDRANT_URL=http://localhost:6333 QDRANT_API_KEY= # Optional, for Qdrant Cloud QDRANT_COLLECTION_NAME=codebase # Default: codebase # Indexing Configuration MAX_FILE_SIZE=1048576 # Maximum file size to index (default: 1MB) BATCH_SIZE=10 # Number of files to process in parallel EMBEDDING_MODEL=text-embedding-3-small # OpenAI embedding model # File Patterns INCLUDE_PATTERNS=**/*.{js,ts,jsx,tsx,py,java,go,rs,cpp,c,h} EXCLUDE_PATTERNS=**/node_modules/**,**/.git/**,**/dist/**

MCP Configuration

Add to your Claude Desktop config (~/.claude/config.json):

{ "mcpServers": { "qdrant-search": { "command": "qdrant-mcp", "args": ["--collection", "my-codebase"], "env": { "OPENAI_API_KEY": "sk-...", "QDRANT_URL": "http://localhost:6333" } } } }

Usage

Command Line Interface

# Index entire codebase qdrant-indexer /path/to/code # Index with custom patterns qdrant-indexer /path/to/code --include "*.py" --exclude "tests/*" # Index specific files qdrant-indexer file1.js file2.py file3.ts # Start background indexer qdrant-control start # Check indexer status qdrant-control status # Stop background indexer qdrant-control stop

In Claude

Once configured, you can use natural language queries:

  • "Find all authentication code"

  • "Show me files that handle user permissions"

  • "What code is similar to the PaymentService class?"

  • "Find all API endpoints related to users"

  • "Show me error handling patterns in the codebase"

Programmatic Usage

from qdrant_mcp_server import QdrantIndexer, QdrantSearcher # Initialize indexer indexer = QdrantIndexer( openai_api_key="sk-...", qdrant_url="http://localhost:6333", collection_name="my-codebase" ) # Index files indexer.index_directory("/path/to/code") # Search searcher = QdrantSearcher( qdrant_url="http://localhost:6333", collection_name="my-codebase" ) results = searcher.search("authentication logic", limit=10) for result in results: print(f"{result.file_path}: {result.score}")

Architecture

┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Claude/MCP │────▶│ MCP Server │────▶│ Qdrant │ │ Client │ │ (Python) │ │ Vector DB │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ ▲ ▼ │ ┌──────────────────┐ │ │ OpenAI API │ │ │ (Embeddings) │──────────────┘ └──────────────────┘

Advanced Configuration

Custom File Processors

from qdrant_mcp_server import FileProcessor class MyCustomProcessor(FileProcessor): def process(self, file_path: str, content: str) -> dict: # Custom processing logic return { "content": processed_content, "metadata": custom_metadata } # Register processor indexer.register_processor(".myext", MyCustomProcessor())

Embedding Models

Support for multiple embedding providers:

# OpenAI (default) indexer = QdrantIndexer(embedding_provider="openai") # Cohere indexer = QdrantIndexer( embedding_provider="cohere", cohere_api_key="..." ) # Local models (upcoming) indexer = QdrantIndexer( embedding_provider="local", model_path="/path/to/model" )

Performance Optimization

Batch Processing

# Process files in larger batches (reduces API calls) qdrant-indexer /path/to/code --batch-size 50 # Limit concurrent requests qdrant-indexer /path/to/code --max-concurrent 5

Incremental Indexing

# Only index changed files since last run qdrant-indexer /path/to/code --incremental # Force reindex of all files qdrant-indexer /path/to/code --force

Cost Estimation

# Estimate indexing costs before running qdrant-indexer /path/to/code --dry-run # Output: # Files to index: 1,234 # Estimated tokens: 2,456,789 # Estimated cost: $0.43

Monitoring

Web UI (Coming Soon)

# Start monitoring dashboard qdrant-mcp --web-ui --port 8080

Logs

# View indexer logs tail -f ~/.qdrant-mcp/logs/indexer.log # View search queries tail -f ~/.qdrant-mcp/logs/queries.log

Metrics

  • Files indexed

  • Tokens processed

  • Search queries per minute

  • Average response time

  • Cache hit rate

Troubleshooting

Common Issues

"Connection refused" error

  • Ensure Qdrant is running: docker ps

  • Check QDRANT_URL is correct

  • Verify firewall settings

"Rate limit exceeded" error

  • Reduce batch size: --batch-size 5

  • Add delay between requests: --delay 1000

  • Use a different OpenAI tier

"Out of memory" error

  • Process fewer files at once

  • Increase Node.js memory: NODE_OPTIONS="--max-old-space-size=4096"

  • Use streaming mode for large files

Debug Mode

# Enable verbose logging qdrant-mcp --debug # Test connectivity qdrant-mcp --test-connection # Validate configuration qdrant-mcp --validate-config

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone the repository git clone https://github.com/kindash/qdrant-mcp-server cd qdrant-mcp-server # Install dependencies npm install pip install -e . # Run tests npm test pytest # Run linting npm run lint flake8 src/

License

MIT License - see LICENSE for details.

Acknowledgments

Support

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