Provides Docker support for containerized deployment of the MCP server with a sample Dockerfile included
Supports Sentence Transformers models from Hugging Face as embedding providers for creating vector representations of content for semantic search
Utilizes OpenAI's embedding models (text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002) to generate vector embeddings for semantic search capabilities
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Qdrant MCP Server find information about machine learning algorithms"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that provides semantic memory capabilities using Qdrant vector database with configurable embedding providers.
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
Related MCP server: Qdrant MCP Server
The server is designed to be lightweight by default. When using OpenAI embeddings:
# For OpenAI embeddings (lightweight, no ML dependencies)
uvx qdrant-mcpFor local embeddings with Sentence Transformers:
# For local embeddings (includes torch and other ML libraries)
uvx --with sentence-transformers qdrant-mcp# 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-transformersThe server can be configured using 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
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)
# 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-v2text-embedding-3-small (1536 dimensions) - Default
text-embedding-3-large (3072 dimensions)
text-embedding-ada-002 (1536 dimensions) - Legacy
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
# Development mode
python -m qdrant_mcp.server
# With MCP CLI
mcp dev src/qdrant_mcp/server.pyStore content with semantic embeddings:
{
"content": "The capital of France is Paris",
"metadata": "{\"category\": \"geography\", \"type\": \"fact\"}",
"id": "optional-custom-id"
}Search for relevant information:
{
"query": "What is the capital of France?",
"limit": 5,
"filter": "{\"category\": \"geography\"}",
"score_threshold": 0.7
}Delete stored items:
{
"ids": "id1,id2,id3"
}List all collections in Qdrant:
{}Get information about the current collection:
{}Add to your Claude Desktop configuration:
{
"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"
}
}
}
}{
"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"
}
}
}
}# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Type checking
mypy src/
# Linting
ruff check src/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.pyFROM python:3.10-slim
WORKDIR /app
COPY . .
RUN pip install -e .
CMD ["python", "-m", "qdrant_mcp.server"]Apache License 2.0
Contributions are welcome! Please feel free to submit a Pull Request.
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Persistent memory for AI agents. Search, store, and recall across sessions.
Universal memory for AI agents and tools. Save, organize and search context anywhere.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/andrewlwn77/qdrant-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server