Qdrant Embedding Search

MCP server that searches documents in Qdrant using embeddings from LMStudio.

Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.

Prerequisites

• Node.js 18+
• LMStudio running with an embedding model loaded (default: text-embedding-qwen3-embedding-4b)
• Qdrant running with a collection containing your documents

Usage

With npx

{
  "mcpServers": {
    "qdrant-docs": {
      "command": "npx",
      "args": ["-y", "mcp-qdrant-embedding-search"],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "QDRANT_COLLECTION": "my_docs",
        "LMSTUDIO_URL": "http://localhost:1234"
      }
    }
  }
}

With node (local)

git clone https://github.com/plixplox/mcp-qdrant-embedding-search.git
cd mcp-qdrant-embedding-search
npm install
npm run build

{
  "mcpServers": {
    "qdrant-docs": {
      "command": "node",
      "args": ["/path/to/mcp-qdrant-embedding-search/dist/index.js"],
      "env": {
        "QDRANT_COLLECTION": "my_docs"
      }
    }
  }
}

Tools

search_docs

Search documents by semantic similarity.

list_collections

List all available Qdrant collections. No parameters.

Configuration

All settings are configured via environment variables:

Custom tool descriptions

Tool names and descriptions are visible to the LLM and affect when it decides to call them. Customize them to match your use case:

{
  "env": {
    "TOOL_SEARCH_NAME": "search_api_reference",
    "TOOL_SEARCH_DESCRIPTION": "Search the REST API reference. Use when you need endpoint specs, request/response schemas, or auth details."
  }
}

License

ISC