mcp-server-qdrantofficial
A
securityA
licenseA
qualityThis repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Last updated -
2
786
Python
Apache 2.0
Provides containerization support for running the MCP server in isolated environments, with Docker Compose configuration included for easy deployment.
Enables configuration of the server through environment variables stored in .env files, supporting settings for connection details and model parameters.
Supports installation from source using Git for developers who want to modify or contribute to the codebase.
Hosts the source code repository, allowing installation directly from GitHub and potentially supporting issue tracking and contributions.
Provides a Makefile with commands for setup, running, testing, formatting, linting, and building the server application.
Provides Python-based installation, configuration, and execution options for deploying and running the MCP server.
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
From source
Configuration
Configuration is done through environment variables. You can create a .env file based on the .env.example file:
Edit the .env file to configure the server:
Usage
Running locally
Or using the make command:
Docker
Tools
The MCP server provides the following tools:
qdrant-store
Stores information in the Qdrant database.
qdrant-find
Searches for information in the Qdrant database using semantic search.
Development
Testing
Formatting
Linting
Building
License
Apache License 2.0
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
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