Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
521,397 tools. Updated 2026-09-06 11:20
"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
- Check engine health and server configuration by retrieving trust state, daemon liveness, event count, allowlist, supported schema, database path, and database existence.MIT
- Integrate a Milvus vector database into an endpoint's context, enabling vector search operations through ctx.MILVUS.Apache 2.0
- Search for similar vectors in Baidu Vector Database tables while filtering results by scalar attributes to find relevant data points efficiently.Apache 2.0
- Rebuilds the search index for all pages to restore search results after switching search engines. Run this once when the new engine returns no results.MIT
Matching MCP Servers
- AlicenseNot gradedqualityDmaintenanceIn-memory vector store with TF-IDF vectorization and cosine similarity search, paid per call via x402 micropayments.MIT
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- Search past tool responses and card templates stored in a vector database. Use natural language, filters, or analytics to retrieve usage history and results.Apache 2.0
- Locate code by natural language meaning, combining semantic vector search with graph analysis to surface relevant files, signatures, and dependencies for initial codebase discovery.MIT
- Search Qdrant collections by vector similarity, passing a query vector or combining multiple prefetch queries with RRF/DBSF fusion for hybrid retrieval.MIT
- Enable semantic search for one explicit keyspace, backfilling records while enforcing access and model constraints. Use it to add vector search to a targeted store, not database-wide.MIT
- Check which search engine the wiki uses to determine whether full-text search is available. Know before trusting search results, as the default indexes only titles and descriptions.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 the health of all memory components to confirm the backend is reachable before issuing recall queries. Provides per-component status for embedding, vector store, and graph database.Apache 2.0
- 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
- 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
- Trigger manual indexing of all context artifacts, chunking and embedding them into the vector database for semantic search.AGPL 3.0
- Set up a database on Coolify with the engine, project, server, and environment. Include engine-specific fields like version, credentials, or memory limits.MIT