mcp-server-qdrant-memory 0.0.1

MCP server for qdrant memory.

MCP Qdrant Memory Server

A Model Context Protocol (MCP) server that provides persistent memory and semantic search capabilities using Qdrant vector database and SentenceTransformer embeddings, built with FastMCP.

Features

Memory Operations

• Store and retrieve documents with semantic search
• Support for multiple text sources (text, raw markdown, headers)
• Automatic text embedding using SentenceTransformer models
• Metadata-based filtering and search capabilities

Collection Management

• Dynamic collection creation and recreation
• Named vector support with configurable dimensions
• Payload indexing for efficient metadata queries
• Automatic schema validation and compatibility checking

Search Capabilities

• Vector Search: Semantic similarity search using text embeddings
• Hybrid Search: Combined vector and metadata filtering
• Filter-Only Search: Pure metadata-based queries without vector search
• Batch Operations: Efficient bulk upsert and deletion

Transport Protocols

• STDIO (default) - For local tools and Claude Desktop integration
• SSE (Server-Sent Events) - For web-based deployments
• Streamable HTTP - Modern HTTP-based protocol

Architecture

The server uses a clean, scalable architecture:

• FastMCP Integration: Modern MCP server framework with multi-transport support
• Qdrant Vector Database: High-performance vector storage and search
• SentenceTransformer: State-of-the-art text embedding generation
• Stable ID Generation: UUIDv5-based consistent document identification
• Flexible Text Sources: Support for various document formats and structures

Installation

Quick Install from PyPI

Once published to PyPI, you can install and run easily:

# Install with uv (recommended)
uvx mcp-server-qdrant-memory  # Run directly without installation

# Or install with pip
pip install mcp-server-qdrant-memory

Install from Source

Prerequisites

Create and activate a virtual environment:

python -m venv venv

# On Windows
.\venv\Scripts\Activate.ps1

# On Linux/macOS
source venv/bin/activate

Basic Installation

Install the project in editable mode:

For Production Use

pip install -e "."

For Development

Install with development tools included:

pip install -e ".[dev]"

Dependencies

Core dependencies (automatically installed):

• mcp>=1.9.4 - Model Context Protocol library
• fastmcp>=2.3.0 - Modern MCP server framework
• qdrant_client>=1.14.3 - Qdrant vector database client
• sentence-transformers>=5.0.0 - Text embedding models

Development dependencies (installed with [dev]):

• pylint - Code linting
• pylint-plugin-utils - Pylint utilities
• pylint-mcp - MCP-specific linting rules
• black - Code formatting

Installation Examples

Quick Start (Production)

# Clone and install
git clone <repository-url>
cd mcp-server-qdrant-memory
python -m venv venv
.\venv\Scripts\Activate.ps1  # Windows
pip install -e "."

Developer Setup

# Clone and setup development environment
git clone <repository-url>
cd mcp-server-qdrant-memory
python -m venv venv
.\venv\Scripts\Activate.ps1  # Windows
pip install -e ".[dev]"

# Run development tools
black src/
pylint src/

Configuration

The server is configured through environment variables:

Required Setup

1. Qdrant Server: Start a Qdrant instance

# Using Docker
docker run -p 6333:6333 qdrant/qdrant

2. Environment Variables (optional, with defaults):

export QDRANT_URL="http://127.0.0.1:6333"           # Qdrant server URL
export QDRANT_API_KEY=""                             # API key (if required)
export QDRANT_COLLECTION_NAME="kakehashi_rag_v2"    # Collection name
export QDRANT_VECTOR_NAME="fast-all-minilm-l6-v2"   # Named vector identifier
export EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"  # Embedding model
export EMBEDDING_BATCH="64"                         # Batch size for embeddings
export MCP_TRANSPORT="stdio"                        # Transport protocol

Usage

Command Line Options

mcp-server-qdrant-memory --help

Development Mode

Use FastMCP's development mode with inspector:

fastmcp dev src/qdrant_memory_server/main.py

MCP Inspector

You can use the MCP Inspector to test and debug your MCP server interactively:

# Install and run MCP Inspector
npx @modelcontextprotocol/inspector

The MCP Inspector provides a web-based interface to:

• Test all available tools
• View tool schemas and documentation
• Debug server responses
• Monitor server logs

Integration Examples

Claude Desktop Integration

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "qdrant-memory": {
      "command": "mcp-server-qdrant-memory",
      "args": [],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT_COLLECTION_NAME": "claude_memory"
      }
    }
  }
}

Or after PyPI publication, use uvx for automatic installation:

{
  "mcpServers": {
    "qdrant-memory": {
      "command": "uvx",
      "args": ["mcp-server-qdrant-memory"],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT_COLLECTION_NAME": "claude_memory"
      }
    }
  }
}