Better Qdrant MCP Server
The Better Qdrant MCP Server manages Qdrant vector databases through semantic search and document operations.
Core Capabilities:
List Collections - View all available Qdrant collections in your database
Add Documents - Process and ingest documents from file paths into collections with configurable chunking (chunk size and overlap)
Semantic Search - Query collections to find similar documents based on semantic meaning with customizable result limits
Delete Collections - Remove collections from your Qdrant database
Embedding Service Support:
OpenAI - Cloud-based embeddings via OpenAI API
OpenRouter - Cloud-based embeddings via OpenRouter API
Ollama - Local embeddings using Ollama models (default: nomic-embed-text)
FastEmbed - Local embeddings using FastEmbed models
Utilizes .env files for configuration of API keys, endpoints, and other server settings.
Requires Node.js runtime environment (version 18.0.0 or higher) to operate the MCP server.
Integrates with Ollama for local embedding models, supporting document embedding and semantic search functionality.
Leverages OpenAI's embedding capabilities for processing and semantically searching documents in Qdrant collections.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Better Qdrant MCP Serversearch for documents about machine learning in my research collection"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: Chroma MCP Server
Installation
npm install -g better-qdrant-mcp-serverOr use it directly with npx:
npx better-qdrant-mcp-serverConfiguration
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:11434Supported 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
Maintenance
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