Qdrant MCP Server

A Model Context Protocol (MCP) server that provides semantic memory capabilities using Qdrant vector database with configurable embedding providers.

Features

Multiple Embedding Providers:
- OpenAI (text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002)
- Sentence Transformers (all-MiniLM-L6-v2, all-mpnet-base-v2, and more)
Semantic Search: Store and retrieve information using vector similarity
Flexible Configuration: Environment variables for all settings
MCP Tools: Store, find, delete, and list operations
Metadata Support: Attach custom metadata to stored content

Installation

Via uvx (Recommended for MCP)

The server is designed to be lightweight by default. When using OpenAI embeddings:

# For OpenAI embeddings (lightweight, no ML dependencies) uvx qdrant-mcp

For local embeddings with Sentence Transformers:

# For local embeddings (includes torch and other ML libraries) uvx --with sentence-transformers qdrant-mcp

Via pip (Development)

# Clone the repository git clone https://github.com/andrewlwn77/qdrant-mcp.git cd qdrant-mcp # Basic install (OpenAI embeddings only) pip install -e . # With local embeddings support pip install -e . sentence-transformers

Configuration

The server can be configured using environment variables:

Required Environment Variables

EMBEDDING_PROVIDER: Choose between openai or sentence-transformers
EMBEDDING_MODEL: Model name for the chosen provider
OPENAI_API_KEY: Required when using OpenAI embeddings

Optional Environment Variables

QDRANT_URL: Qdrant server URL (default: http://localhost:6333)
QDRANT_API_KEY: Qdrant API key (optional)
COLLECTION_NAME: Qdrant collection name (default: mcp_memory)
DEVICE: Device for sentence transformers (default: auto-detect)
DEFAULT_LIMIT: Default search results limit (default: 10)
SCORE_THRESHOLD: Minimum similarity score (default: 0.0)

Example Configuration

# OpenAI embeddings export EMBEDDING_PROVIDER=openai export EMBEDDING_MODEL=text-embedding-3-small export OPENAI_API_KEY=your-api-key # Sentence Transformers (local) export EMBEDDING_PROVIDER=sentence-transformers export EMBEDDING_MODEL=all-MiniLM-L6-v2

Supported Embedding Models

OpenAI Models

text-embedding-3-small (1536 dimensions) - Default
text-embedding-3-large (3072 dimensions)
text-embedding-ada-002 (1536 dimensions) - Legacy

Sentence Transformers Models

all-MiniLM-L6-v2 (384 dimensions) - Fast and efficient
all-mpnet-base-v2 (768 dimensions) - Higher quality
Any other Sentence Transformers model from Hugging Face

Usage

Starting the Server

# Development mode python -m qdrant_mcp.server # With MCP CLI mcp dev src/qdrant_mcp/server.py

MCP Tools

qdrant-store

Store content with semantic embeddings:

{ "content": "The capital of France is Paris", "metadata": "{\"category\": \"geography\", \"type\": \"fact\"}", "id": "optional-custom-id" }

qdrant-find

Search for relevant information:

{ "query": "What is the capital of France?", "limit": 5, "filter": "{\"category\": \"geography\"}", "score_threshold": 0.7 }

qdrant-delete

Delete stored items:

{ "ids": "id1,id2,id3" }

qdrant-list-collections

List all collections in Qdrant:

{}

qdrant-collection-info

Get information about the current collection:

{}

Integration with Claude Desktop

Add to your Claude Desktop configuration:

For OpenAI Embeddings (Lightweight)

{ "mcpServers": { "qdrant-memory": { "command": "uvx", "args": ["qdrant-mcp"], "env": { "EMBEDDING_PROVIDER": "openai", "EMBEDDING_MODEL": "text-embedding-3-small", "OPENAI_API_KEY": "your-api-key", "QDRANT_URL": "https://your-instance.qdrant.io", "QDRANT_API_KEY": "your-qdrant-api-key" } } } }

For Local Embeddings (Sentence Transformers)

{ "mcpServers": { "qdrant-memory": { "command": "uvx", "args": ["--with", "sentence-transformers", "qdrant-mcp"], "env": { "EMBEDDING_PROVIDER": "sentence-transformers", "EMBEDDING_MODEL": "all-MiniLM-L6-v2", "QDRANT_URL": "https://your-instance.qdrant.io", "QDRANT_API_KEY": "your-qdrant-api-key" } } } }

Development

Running Tests

# Install dev dependencies pip install -e ".[dev]" # Run tests pytest # Type checking mypy src/ # Linting ruff check src/

Project Structure

qdrant-mcp/ ├── src/ │ └── qdrant_mcp/ │ ├── __init__.py │ ├── server.py # MCP server implementation │ ├── settings.py # Configuration management │ ├── qdrant_client.py # Qdrant operations │ └── embeddings/ │ ├── base.py # Abstract base class │ ├── factory.py # Provider factory │ ├── openai.py # OpenAI implementation │ └── sentence_transformers.py # ST implementation └── tests/ └── test_server.py

Docker Support

FROM python:3.10-slim WORKDIR /app COPY . . RUN pip install -e . CMD ["python", "-m", "qdrant_mcp.server"]

License

Apache License 2.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Qdrant MCP Server

Qdrant MCP Server

Features

Installation

Via uvx (Recommended for MCP)

Via pip (Development)

Configuration

Required Environment Variables

Optional Environment Variables

Example Configuration

Supported Embedding Models

OpenAI Models

Sentence Transformers Models

Usage

Starting the Server

MCP Tools

qdrant-store

qdrant-find

qdrant-delete

qdrant-list-collections

qdrant-collection-info

Integration with Claude Desktop

For OpenAI Embeddings (Lightweight)

For Local Embeddings (Sentence Transformers)

Development

Running Tests

Project Structure

Docker Support

License

Contributing

Related Resources

Appeared in Searches

New MCP Servers

MCP directory API