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Better Qdrant MCP Server
A Model Context Protocol (MCP) server for enhanced Qdrant vector database functionality. This server provides tools for managing Qdrant collections, adding documents, and performing semantic searches.
Features
- List Collections: View all available Qdrant collections
- Add Documents: Process and add documents to a Qdrant collection with various embedding services
- Search: Perform semantic searches across your vector database
- Delete Collection: Remove collections from your Qdrant database
Installation
npm install -g better-qdrant-mcp-server
Or use it directly with npx:
npx better-qdrant-mcp-server
Configuration
The server uses environment variables for configuration. You can set these in a .env
file in your project root:
# Qdrant Configuration
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your_api_key_if_needed
# Embedding Service API Keys
OPENAI_API_KEY=your_openai_api_key
OPENROUTER_API_KEY=your_openrouter_api_key
OLLAMA_ENDPOINT=http://localhost:11434
Supported Embedding Services
- OpenAI: Requires an API key
- OpenRouter: Requires an API key
- Ollama: Local embedding models (default endpoint: http://localhost:11434)
- FastEmbed: Local embedding models
Usage with Claude
To use this MCP server with Claude, add it to your MCP settings configuration file:
{
"mcpServers": {
"better-qdrant": {
"command": "npx",
"args": ["better-qdrant-mcp-server"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"QDRANT_API_KEY": "your_api_key_if_needed",
"DEFAULT_EMBEDDING_SERVICE": "ollama",
"OPENAI_API_KEY": "your_openai_api_key",
"OPENAI_ENDPOINT": "https://api.openai.com/v1",
"OPENROUTER_API_KEY": "your_openrouter_api_key",
"OPENROUTER_ENDPOINT": "https://api.openrouter.com/v1",
"OLLAMA_ENDPOINT": "http://localhost:11434",
"OLLAMA_MODEL": "nomic-embed-text"
}
}
}
}
Example Commands
List Collections
use_mcp_tool
server_name: better-qdrant
tool_name: list_collections
arguments: {}
Add Documents
use_mcp_tool
server_name: better-qdrant
tool_name: add_documents
arguments: {
"filePath": "/path/to/your/document.pdf",
"collection": "my-collection",
"embeddingService": "openai",
"chunkSize": 1000,
"chunkOverlap": 200
}
Search
use_mcp_tool
server_name: better-qdrant
tool_name: search
arguments: {
"query": "your search query",
"collection": "my-collection",
"embeddingService": "openai",
"limit": 5
}
Delete Collection
use_mcp_tool
server_name: better-qdrant
tool_name: delete_collection
arguments: {
"collection": "my-collection"
}
Requirements
- Node.js >= 18.0.0
- A running Qdrant server (local or remote)
- API keys for the embedding services you want to use
License
MIT
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Reviews

user_vTqqhNVK
I have been using Model Context Protocol (MCP) Types by ghbountybot, and it has significantly enhanced my workflow. The structured and intuitive design of MCP Types ensures seamless integration and boosts efficiency. Highly recommended for anyone in need of a reliable and efficient protocol model!