Better Qdrant

Better Qdrant

wrediam

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.

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What it does

  • List Qdrant collections
  • Add documents to vector collections
  • Perform semantic searches
  • Delete collections
  • Generate embeddings with multiple services

Best for

Building RAG applications with document searchAI developers working with vector databasesSemantic search implementations
Multiple embedding providers (OpenAI, Ollama, FastEmbed)Local embedding options available

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.

Better Qdrant Server MCP server

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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