Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
676,588 tools. Updated 2026-10-19 12:32
"Qdrant vector database and search engine" matching MCP tools:
- Convert a skill directory into Qdrant vector database format, enabling high-performance search with native payload filtering.MIT
- Probe vector database endpoints for unauthenticated exposure, fingerprinting Weaviate, Qdrant, Chroma, Elasticsearch, Redis, and Postgres/pgvector and listing exposed schemas or data read-only.MIT
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Extract clean, tag-free plain text from any webpage for RAG embedding and vector indexing. Returns title, word count, and token metrics.MIT
- Integrate a Milvus vector database into an endpoint's context, enabling vector search operations through ctx.MILVUS.Apache 2.0
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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.21,548Apache 2.0- AlicenseNot gradedqualityDmaintenanceAn MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.MIT
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MobilityData's open catalogue of public-transport feeds — ~4,500 GTFS schedule feeds and ~1,900…
Operate your Vector Panda collections: search and upsert vectors, schema, measured indexes, exports.
- Retrieve database engine, name, memory, address, and port by providing the database ID.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
- Reindex all pages in the active search engine to restore search results after switching from the basic search engine. Run once to populate the new empty index.MIT
- Search Qdrant collections by vector similarity, passing a query vector or combining multiple prefetch queries with RRF/DBSF fusion for hybrid retrieval.MIT
- List the search engine configured for the wiki to verify whether page content is indexed before relying on search results.MIT
- Create a new database connection in Apache Superset by providing connection details like SQLAlchemy URI, engine type, and database name to enable data visualization.MIT
- 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
- Check OpenLMlib database and vector index status to debug errors, verify initialization, or assess system readiness. Returns database size, finding count, and index status.MIT
- Verify health of embedding provider, Qdrant vector store, and Memgraph to confirm the memory backend is reachable before issuing recall queries.Apache 2.0
- Set up the project log search index in Azure AI Search with vector search support for semantic similarity. Safely creates schema if missing, or confirms existing without changes.MIT
- List all Qdrant collections with their point counts to inspect and manage vector store contents.Apache 2.0
- Discover all vector columns in your PostgreSQL database, including table names and vector dimensions. Optionally filter by schema.MIT
- Save research content to a vector database for semantic search and future retrieval, organized by topic for easy access.MIT
- List database agent templates by engine type, Docker images, and CPU/memory/disk allocations. Supports pagination, search, and full detail views.MIT
- Trigger manual indexing of all context artifacts, chunking and embedding them into the vector database for semantic search.AGPL 3.0