Qdrant Retrieve MCP Server
About
MCP server for semantic search with Qdrant vector database
Details
- Author
- gergelyszerovay
- Downloads
- 317
- Categories
- Search, Other, Knowledge Base, Database
Jump to
- Semantic search across multiple Qdrant collections
- Multi-query support (array of query texts)
- Configurable result count (topK)
- Collection source tracking in results
- Stdio and HTTP transport options
- Optional REST API server
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Qdrant Retrieve MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Configure it in Claude Desktop by adding an MCP server entry with the command npx -y @gergelyszerovay/mcp-server-qdrant-retrive and optionally set the QDRANT_API_KEY environment variable. Alternatively, run it from the command line using flags like --qdrantUrl, --enableHttpTransport, or --embeddingModelType.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"qdrant retrieve mcp server": {
"qdrant": {
"command": "npx",
"args": [
"-y",
"@gergelyszerovay/mcp-server-qdrant-retrive"
],
"env": {
"QDRANT_API_KEY": "your_api_key_here"
}
}
}
}
}McpServers
{
"qdrant": {
"command": "npx",
"args": [
"-y",
"@gergelyszerovay/mcp-server-qdrant-retrive"
],
"env": {
"QDRANT_API_KEY": "your_api_key_here"
}
}
}MCP server for semantic search with Qdrant vector database.
- Semantic search across multiple collections
- Multi-query support
- Configurable result count
- Collection source tracking
Note: The server connects to a Qdrant instance specified by URL.
Note 2: The first retrieve might be slower, as the MCP server downloads the required embedding model.
- qdrant_retrieve
- Retrieves semantically similar documents from multiple Qdrant vector store collections based on multiple queries
- Inputs:
- collectionNames(string[]): Names of the Qdrant collections to search across
- topK(number): Number of top similar documents to retrieve (default: 3)
- query(string[]): Array of query texts to search for
- results: Array of retrieved documents with:
- query: The query that produced this result
- collectionName: Collection name that this result came from
- text: Document text content
- score: Similarity score between 0 and 1
Add this to yourclaude_desktop_config.json:
{ "mcpServers": { "qdrant": { "command": "npx", "args": ["-y", "@gergelyszerovay/mcp-server-qdrant-retrive"], "env": { "QDRANT_API_KEY": "your_api_key_here" } } } }
MCP server for semantic search with Qdrant vector database. Options --enableHttpTransport Enable HTTP transport [default: false] --enableStdioTransport Enable stdio transport [default: true] --enableRestServer Enable REST API server [default: false] --mcpHttpPort=<port> Port for MCP HTTP server [default: 3001] --restHttpPort=<port> Port for REST HTTP server [default: 3002] --qdrantUrl=<url> URL for Qdrant vector database [default: http://localhost:6333] --embeddingModelType=<type> Type of embedding model to use [default: Xenova/all-MiniLM-L6-v2] --help Show this help message Environment Variables QDRANT_API_KEY API key for authenticated Qdrant instances (optional) Examples $ mcp-qdrant --enableHttpTransport $ mcp-qdrant --mcpHttpPort=3005 --restHttpPort=3006 $ mcp-qdrant --qdrantUrl=http://qdrant.example.com:6333 $ mcp-qdrant --embeddingModelType=Xenova/all-MiniLM-L6-v2
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