Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
466,097 tools. Updated 2026-08-19 11:00
"Qdrant vector database and search engine" matching MCP tools:
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- Check health of all memory components: embedding provider, Qdrant vector store, and optional Memgraph graph database. Returns JSON status to verify backend connectivity before recall queries.Apache 2.0
- Get a list of supported database engine types, such as PostgreSQL, MySQL, and SQLite, to select the right engine when creating a new database connection in Superset.MIT
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- Retrieve active embedding backend and model status to confirm vector-search readiness.MIT
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- Query a ChromaDB vector database to retrieve relevant documents using LangChain integration. Specify the persist directory and number of results.MIT
- Retrieve relevant information from a long-term knowledge base using semantic search. Access context, facts, or past interactions stored in Qdrant.MIT