MCP Server for Qdrant: Scalable Vector DBs & Enterprise Search

MCP Server for Qdrant: Deploy scalable vector databases seamlessly. Boost AI/ML performance with enterprise-grade search—no compromises.

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About MCP Server for Qdrant

What is MCP Server for Qdrant: Scalable Vector DBs & Enterprise Search?

MCP Server for Qdrant is a purpose-built solution for managing vectorized data storage and semantic search operations using the Qdrant vector database. It provides a streamlined interface to store text-based information with metadata, perform high-precision searches, and integrate seamlessly with embedding services like FastEmbed. Designed for enterprise use cases, it supports multi-environment configurations and Docker-based deployments to ensure scalability and operational flexibility.

How to Use MCP Server for Qdrant: Scalable Vector DBs & Enterprise Search?

Usage follows a three-step workflow:

Installation: Deploy via pip install mcp-server-qdrant or compile from source using Git and Makefile workflows.
Configuration: Set up environment variables in a .env file specifying Qdrant endpoints, collection names, and embedding provider details. Docker configurations are auto-mapped via compose files.
Execution: Run locally using Python execution commands or Docker orchestration. Use the qdrant-store tool for data ingestion and qdrant-find for semantic search operations.

MCP Server for Qdrant Features

Key Features of MCP Server for Qdrant: Scalable Vector DBs & Enterprise Search?

Semantic Search Layer: Leverages Qdrant's vector similarity algorithms for context-aware text retrieval
Embedding Integration: Native support for FastEmbed services with customizable model configurations
Environment-Aware Configs: Simplifies multi-stage deployments through granular variable management
Production-Ready Deployment: Docker support ensures consistent runtime environments across infrastructure
Metadata Enrichment: Stores structured metadata alongside text vectors for contextual filtering

Use Cases of MCP Server for Qdrant: Scalable Vector DBs & Enterprise Search?

Common applications include:

Enterprise knowledge graphs with version-controlled document embeddings
Customer support systems using semantic ticket categorization
Product recommendation engines with dynamic metadata filtering
Real-time analytics pipelines for vectorized log analysis
Multi-tenant search infrastructure via collection-based isolation

MCP Server for Qdrant FAQ

FAQ from MCP Server for Qdrant: Scalable Vector DBs & Enterprise Search?

How does it handle scaling?: Supports horizontal scaling through Qdrant's distributed architecture and Docker orchestration
What embedding models are supported?: Any FastEmbed-compatible models, with MiniLM variants pre-configured
Can I use custom collection names?: Yes, via the COLLECTION_NAME environment variable
Are there performance guarantees?: Optimized for vector similarity searches at scale, with latency under 100ms for typical deployments
Where can I find advanced documentation?: See official Qdrant docs for vector storage specifics

Content

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

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