Qdrant MCP Server

Official MCP server for Qdrant vector search engine. Acts as a semantic memory layer enabling AI agents to store and retrieve information using vector similarity search. Supports storing text with metadata, semantic querying, configurable embedding models via FastEmbed, and both cloud-hosted and local Qdrant instances. Useful for building RAG pipelines, code search, knowledge bases, and long-term agent memory.

{
  "mcpServers": {
    "qdrant": {
      "command": "uvx",
      "args": [
        "mcp-server-qdrant"
      ],
      "env": {
        "QDRANT_URL": "<YOUR_QDRANT_URL>",
        "QDRANT_API_KEY": "<YOUR_QDRANT_API_KEY>",
        "COLLECTION_NAME": "<YOUR_COLLECTION_NAME>",
        "EMBEDDING_MODEL": "<YOUR_EMBEDDING_MODEL>"
      }
    }
  }
}

{
  "type": "object",
  "required": [
    "information"
  ],
  "properties": {
    "metadata": {
      "type": "object",
      "description": "Optional key-value metadata to attach to the stored entry"
    },
    "information": {
      "type": "string",
      "description": "Text information to store and index"
    },
    "collection_name": {
      "type": "string",
      "description": "Name of the collection to store in (required if no default set)"
    }
  }
}

{
  "type": "object",
  "required": [
    "query"
  ],
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural language query to search for"
    },
    "collection_name": {
      "type": "string",
      "description": "Name of the collection to search in (required if no default set)"
    }
  }
}