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

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

• Built for the Model Context Protocol
• Powered by Qdrant vector database
• Embeddings by OpenAI
• Originally developed for KinDash

Support

• 📧 Email: support@kindash.app
• 💬 Discord: Join our community
• 🐛 Issues: GitHub Issues
• 📖 Docs: Full Documentation