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.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

Provides semantic memory capabilities using Qdrant vector database with configurable embedding providers, allowing storage and retrieval of information using vector similarity.

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