About Better Qdrant MCP Server
Better Qdrant MCP Server provides tools for managing Qdrant collections, adding documents, and searching vector data semantically. It supports OpenAI, OpenRouter, Ollama, and FastEmbed for embeddings.
Key features
- 01List Qdrant collections
- 02Add documents with embeddings
- 03Search collections semantically
- 04Delete collections
- 05OpenAI, OpenRouter, Ollama, and FastEmbed support
Use cases
- 01Reviewing collections in a Qdrant database
- 02Adding documents to a collection
- 03Searching indexed documents by meaning
- 04Removing an unused collection
MCP support
The MCP server exposes tools to list and delete Qdrant collections, add documents, and run semantic searches.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
4Saved tool list · updated 3w ago
This tool takes no parameters.
Details
From the project READMEBetter 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
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.