
Better Qdrant
Connects to Qdrant vector databases to store documents and perform semantic searches using various embedding services. Enables AI systems to manage vector collections and find similar documents through natural language queries.
Connects AI systems to Qdrant vector database for semantic search capabilities through multiple embedding services, enabling efficient document management and similarity searches without leaving the conversation interface.
What it does
- List Qdrant collections
- Add documents to vector collections
- Perform semantic searches
- Delete collections
- Generate embeddings with multiple services
Best for
About Better Qdrant
Better Qdrant is a community-built MCP server published by wrediam that provides AI assistants with tools and capabilities via the Model Context Protocol. Better Qdrant connects AI to Qdrant vector database, enabling seamless semantic search and efficient document management It is categorized under databases, ai ml.
How to install
You can install Better Qdrant in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Better Qdrant is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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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