Qdrant MCP Server: Lightning-Fast Vector Search & Scalable Storage

Qdrant MCP Server

An MCP server for interacting with Qdrant vector database. This server provides tools for managing vectors, performing similarity searches, and automatic text-to-vector embedding using the MCP (Master Control Program) framework.

Features

• Automatic text-to-vector embedding using FastEmbed
• Store and retrieve text content with vector search
• Use default collection configuration through environment variables
• Text similarity search by content
• Efficient embedding with optimized models

Configuration

Create a .env file based on the .env.example template:

# Qdrant connection settings
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_API_KEY=
QDRANT_VERIFY_SSL=True  # Set to False if using self-signed certificates

# Default settings
DEFAULT_COLLECTION_NAME=default_collection
EMBEDDING_MODEL=BAAI/bge-small-en-v1.5

You can change the embedding model to any model supported by FastEmbed.

Usage

Running locally

1. Install the package:

pip install -e .

2. Run the server:

qdrant-mcp-server

Running with Docker

1. Build the Docker image:

docker build -t qdrant-mcp-server .

2. Run the container:

docker run -p 8000:8000 --env QDRANT_HOST=<your-qdrant-host> --env QDRANT_PORT=<your-qdrant-port> --env QDRANT_VERIFY_SSL=<True|False> qdrant-mcp-server

Testing

This package includes a test suite to validate the functionality. To run the tests:

1. Install development dependencies:

pip install -e ".[dev]"

2. Run the tests:

cd tests
./run_tests.py

Alternatively, you can use pytest directly:

pytest -xvs tests/

Using Self-Signed Certificates

If your Qdrant server uses a self-signed certificate, set QDRANT_VERIFY_SSL=False in your .env file or when running the Docker container. This disables SSL certificate verification.

Tools

The server provides the following tools:

Text Tools

• store_text: Convert text to an embedding vector and store it in the database
• search_similar_text: Convert query text to an embedding and find similar vectors
• store_texts: Convert multiple texts to embeddings and store them in batch

Vector Tools

• search_vectors: Search for similar vectors in a collection
• upsert_vectors: Upload vectors to a collection
• filter_search: Search collection with metadata filters

Point Tools

• get_points: Get points by their IDs from a collection
• delete_points: Delete points by their IDs from a collection
• count_points: Count the number of points in a collection

Examples

Storing text

await store_text(
    text="What is the capital of France?", 
    metadata={"category": "geography", "type": "question"}
)

Searching for similar text

await search_similar_text(
    query="What is Paris the capital of?",
    limit=5
)

Storing multiple texts

await store_texts(
    texts=["Paris is in France", "London is in England", "Berlin is in Germany"],
    metadatas=[
        {"category": "geography", "country": "France"},
        {"category": "geography", "country": "England"},
        {"category": "geography", "country": "Germany"}
    ]
)