Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
213,524 tools. Last updated 2026-06-19 18:25
"Qdrant vector database and search engine" matching MCP tools:
- Export skills to Qdrant vector database for high-performance search and native payload filtering. Transforms source types into AI-ready skills and RAG knowledge.MIT
- Search for similar vectors in Baidu Vector Database tables while filtering results by scalar attributes to find relevant data points efficiently.Apache 2.0
- Verify memory backend reachability by checking health of embedding provider, Qdrant, and Memgraph components. Returns JSON with overall and per-component status.Apache 2.0
- Save research content to a vector database for semantic search and future retrieval, organized by topic for easy access.MIT
- Indexes or re-indexes context artifacts defined in .socraticodecontextartifacts.json. Chunks and embeds content into a vector database for semantic search.AGPL 3.0
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mcp-server-qdrantofficial
AlicenseBqualityDmaintenanceThis repository is an example of how to create a MCP server for Qdrant, a vector search engine.Last updated21,434Apache 2.0- Alicense-qualityDmaintenanceEnables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.Last updated9MIT
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Search PubMed and summarize biomedical literature — designed for AI health agents.
- Ingest documents (PDF, DOCX, TXT, MD) into a vector database for semantic search. Supports updating existing documents and visual captioning for PDF figures.MIT
- Retrieve comprehensive metadata for a database, including its engine, features, and settings, using the database ID.MIT
- Ingest documents (PDF, DOCX, TXT, MD) into a local vector database for semantic search. Supports re-ingestion to update existing content.MIT
- Retrieve a list of all collections in your Qdrant server to view and manage available vector spaces.Apache 2.0
- Create a Databricks Vector Search endpoint by specifying its name and type (STANDARD or OPTIMIZED_STORAGE).MIT
- Tokenize an Odoo record by building a composite document, embedding it, and storing the vector in Qdrant for semantic search.AGPL 3.0
- Retrieve Qdrant collection details including vector size, distance metric, point and indexed vector counts, plus Odoo-side indexed document count for cross-checking.AGPL 3.0
- Get a list of Vector Search endpoints from your Databricks workspace.MIT
- Save new documents or text chunks to a Qdrant vector database for later retrieval, adding information to a knowledge base with metadata support.Apache 2.0
- Export your skills to Weaviate vector database format, enabling hybrid vector and BM25 keyword search for production RAG applications.MIT
- Re-embed all memories in the vector database to refresh and update stored information for accurate retrieval.MIT
- Retrieve vector database statistics including stored context count and dimensions to monitor data volume and structure.MIT
- Retrieve all available Qdrant collections for managing and organizing vector database content in the Better Qdrant MCP Server.MIT
- Search a Qdrant collection by providing a raw vector to find similar items based on vector similarity, without needing a text query.Apache 2.0