Qdrant Vector Database

A Machine Control Protocol (MCP) server for storing and retrieving information from a Qdrant vector database.

Features

• Store text information with optional metadata in Qdrant
• Semantic search for stored information
• FastEmbed integration for text embeddings
• Environment-based configuration
• Docker support

Installation

Using pip

pip install mcp-server-qdrant

From source

git clone https://github.com/your-org/mcp-server-qdrant.git
cd mcp-server-qdrant
make setup

Configuration

Configuration is done through environment variables. You can create a .env file based on the .env.example file:

cp .env.example .env

Edit the .env file to configure the server:

# Qdrant configuration
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your-api-key

# Collection name
COLLECTION_NAME=memories

# Embedding provider configuration
EMBEDDING_PROVIDER=fastembed
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

Usage

Running locally

python -m mcp_server_qdrant.main

Or using the make command:

make run

Docker

docker-compose up

Tools

The MCP server provides the following tools:

qdrant-store

Stores information in the Qdrant database.

information: The text to store
metadata: Optional JSON metadata to associate with the text

qdrant-find

Searches for information in the Qdrant database using semantic search.

query: The search query

Development

Testing

make test

Formatting

make format

Linting

make lint

Building

make build

License

Apache License 2.0