MCP-Qdrant Server provides a vector database integration for AI assistants, combining a Qdrant vector database with a specialized server that enables knowledge storage and retrieval. The implementation uses Docker containers to run both the Qdrant database and the MCP server, with the server utilizing the sentence-transformers embedding model to convert natural language into vector representations. It exposes two primary tools: one for storing code snippets with natural language descriptions, and another for searching the knowledge base using semantic queries. This setup is particularly useful for AI assistants that need to maintain persistent memory of code examples and technical knowledge across conversations.
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Stores information in the Qdrant database. Parameters: information (string), metadata (JSON optional). Returns a confirmation message.
Searches for related information in the Qdrant database. Parameter: query (string). Returns stored information from Qdrant database as individual messages.
