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MCP Server for Qdrant

MCP Server for Qdrant

Local
github.com/Jimmy974Databases0Updated 84w ago

About MCP Server for Qdrant

MCP Server for Qdrant connects MCP clients to a Qdrant vector database for storing text and retrieving it with semantic search. It is suited to applications that need searchable text records with optional metadata.

Key features

  1. 01Store text with optional metadata
  2. 02Semantic search in Qdrant
  3. 03FastEmbed text embeddings
  4. 04Environment-based configuration
  5. 05Docker support

Use cases

  1. 01Save information for later retrieval
  2. 02Find stored text by meaning

MCP support

Exposes qdrant-store and qdrant-find tools to MCP clients for writing information to Qdrant and searching it semantically.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Server for Qdrant

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

Comments

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