MCP Server Qdrant Retrive
A semantic search MCP service based on the Qdrant vector database, supporting multi - collection retrieval and configurable result return
rating : 2 points
downloads : 15
What is the Qdrant Retrieve MCP Server?
This is a semantic search service based on the Qdrant vector database. It can understand the deep meaning of query statements and intelligently retrieve relevant content from multiple document collections.How to use this service?
You can achieve the intelligent document retrieval function through simple API calls or by integrating it into Claude Desktop.Applicable scenarios
It is suitable for scenarios that require semantic understanding rather than simple keyword matching, such as knowledge base search, intelligent document recommendation, and question - answering systems.Main features
Multi - collection searchIt can search multiple document collections simultaneously and return unified results
Multi - query supportIt supports submitting multiple related queries at once to improve retrieval efficiency
Configurable number of resultsYou can customize the number of search results returned to meet different needs
Source trackingIt clearly marks the source collection of each result for easy subsequent processing
Advantages and limitations
Advantages
Understand query semantics rather than simple keyword matching
Support fast retrieval of large - scale document collections
Flexible API interfaces for easy integration
Automatically handle the text vectorization process
Limitations
The first retrieval requires downloading the embedding model, which may be slow
Requires pre - configuring the Qdrant database connection
Depends on external vector database services
How to use
Configure Claude Desktop
Add the MCP server configuration to claude_desktop_config.json
Start the service
Start the MCP server through the command line
Execute a search
Send a search request through the API
Usage examples
Enterprise knowledge base searchFind relevant technical documents from multiple internal company knowledge bases
Product support document retrievalFind answers to user questions from product manuals and FAQs
Frequently Asked Questions
Why is the first retrieval slow?
How to improve search quality?
Which languages are supported for document retrieval?
Related resources
Qdrant official documentation
Official documentation for the Qdrant vector database
HuggingFace model library
Details of the default embedding model used
MCP protocol specification
Official specification of the Model Context Protocol
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