Qdrant Retrieve
About
Enables semantic search across multiple document collections using Qdrant vector database integration, allowing natural language queries with configurable result counts and collection tracking.
Details
- Author
- gergelyszerovay
- Repository
- gergelyszerovay/mcp-server-qdrant-retrieve
- Downloads
- 236
- License
- MIT License
- Categories
- Productivity, Workplace, File Management, AI, Search, Knowledge Base, API, Infrastructure
- Tags
- #web
Jump to
- 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.
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 RetrieveCommand (node, npx, python, etc.)npxArguments-
Argument 1
-y -
Argument 2
@gergelyszerovay/mcp-server-qdrant-retrive
Environment-
QDRANT_API_KEY
your_api_key_here
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"qdrant": {
"command": "npx",
"args": ["-y", "@gergelyszerovay/mcp-server-qdrant-retrive"],
"env": {
"QDRANT_API_KEY": "your_api_key_here"
}
}
}
}
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. Returns: results: Array of retrieved documents with: query, collectionName, text, score.
- 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
- Returns:
- 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
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"qdrant retrieve": {
"env": {
"QDRANT_API_KEY": "your_api_key_here"
},
"args": [
"-y",
"@gergelyszerovay/mcp-server-qdrant-retrive"
],
"command": "npx"
}
}
}Linux
{
"env": {
"QDRANT_API_KEY": "your_api_key_here"
},
"args": [
"-y",
"@gergelyszerovay/mcp-server-qdrant-retrive"
],
"command": "npx"
}Macos
{
"env": {
"QDRANT_API_KEY": "your_api_key_here"
},
"args": [
"-y",
"@gergelyszerovay/mcp-server-qdrant-retrive"
],
"command": "npx"
}Windows
{
"env": {
"QDRANT_API_KEY": "your_api_key_here"
},
"args": [
"/c",
"npx",
"-y",
"@gergelyszerovay/mcp-server-qdrant-retrive"
],
"command": "cmd"
}Qdrant Retrieve MCP Server
MCP server for semantic search with Qdrant vector database.
Features
- 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.
API
Tools
- 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
- Returns:
- 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
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"qdrant": {
"command": "npx",
"args": ["-y", "@gergelyszerovay/mcp-server-qdrant-retrive"],
"env": {
"QDRANT_API_KEY": "your_api_key_here"
}
}
}
}
Command Line Options
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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