by gergelyszerovay
Perform semantic search across collections with Qdrant Retrieve, powered by vector database integration and natural lang
Performs semantic search across document collections stored in Qdrant vector database using natural language queries.
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.
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.
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.
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
I recommend Qdrant Retrieve for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Qdrant Retrieve benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: Qdrant Retrieve is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated Qdrant Retrieve against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend Qdrant Retrieve for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated Qdrant Retrieve against two servers with overlapping tools; this profile had the clearer scope statement.
Qdrant Retrieve has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Qdrant Retrieve is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Qdrant Retrieve reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Strong directory entry: Qdrant Retrieve surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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MCP server for semantic search with Qdrant vector database.
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.
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 forresults: 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 1Add 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"
}
}
}
}
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
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ Use when
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid when
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.