Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
205,128 tools. Last updated 2026-06-15 15:37
"Using Qdrant vector database for code indexing" matching MCP tools:
- 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
- Indexes a codebase to build vector embeddings for semantic code search across 50+ languages. Required initial step before searching code.MIT
- Safely remove a project's codebase index by stopping file watcher, canceling active indexing, and waiting for in-flight graph build before deletion.AGPL 3.0
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT
- Analyze source code complexity with AST-based metrics including cyclomatic complexity, nesting depth, and function counts. No prior indexing needed.MIT
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- Remove all indexed context artifacts for a project from the vector database. Requires indexing to be stopped before use.AGPL 3.0
- Retrieve a list of all collections in your Qdrant server to view and manage available vector spaces.Apache 2.0
- 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
- 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
- Remove a document from Chroma vector database using its unique ID. This tool ensures efficient document management and cleanup in the MCP server environment.MIT
- Re-embed all memories in the vector database to refresh and update stored information for accurate retrieval.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
- Create, list, delete collections and add documents in a vector database to support RAG operations.MIT
- Index a local project directory into a vector database to enable semantic code search and analysis across your codebase.MIT
- Retrieve vector database configuration details to understand storage setup and manage embeddings for AI applications.MIT
- Remove a specific collection from the Qdrant vector database to manage storage and optimize semantic search operations effectively.MIT