Qdrant Retrieve▌

by gergelyszerovay
Perform semantic search across collections with Qdrant Retrieve, powered by vector database integration and natural lang
Enables semantic search across multiple document collections using Qdrant vector database integration, allowing natural language queries with configurable result counts and collection tracking.
best for
- / AI applications needing document retrieval
- / Building semantic search features
- / RAG (Retrieval Augmented Generation) systems
capabilities
- / Search documents using natural language queries
- / Retrieve results from multiple Qdrant collections
- / Configure number of search results returned
- / Track and manage different document collections
what it does
Performs semantic search across document collections stored in Qdrant vector database using natural language queries.
about
Qdrant Retrieve is a community-built MCP server published by gergelyszerovay that provides AI assistants with tools and capabilities via the Model Context Protocol. Perform semantic search across collections with Qdrant Retrieve, powered by vector database integration and natural lang It is categorized under databases, ai ml.
how to install
You can install Qdrant Retrieve in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Qdrant Retrieve is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
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 acrosstopK(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 resultcollectionName: Collection name that this result came fromtext: Document text contentscore: 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
FAQ
- What is the Qdrant Retrieve MCP server?
- Qdrant Retrieve is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Qdrant Retrieve?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
Qdrant Retrieve is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated Qdrant Retrieve against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: Qdrant Retrieve is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
Qdrant Retrieve reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend Qdrant Retrieve for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: Qdrant Retrieve surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
Qdrant Retrieve has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, Qdrant Retrieve benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired Qdrant Retrieve into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
Qdrant Retrieve is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.