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

Install the package:

pip install -e .

Run the server:

qdrant-mcp-server

Running with Docker

Build the Docker image:

docker build -t qdrant-mcp-server .

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:

Install development dependencies:

pip install -e ".[dev]"

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"}
    ]
)

qdrant-mcp-server