Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
675,125 tools. Updated 2026-10-19 03:16
"Using Qdrant vector database for code indexing" matching MCP tools:
- Convert a skill directory into Qdrant vector database format, enabling high-performance search with native payload filtering.MIT
- Add an evidence bundle to a collection for semantic search and Q&A. Triggers async vector indexing; poll job status until complete.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
- Retrieve vertices and their vector attributes using GSQL when the REST API cannot fetch vectors. Specify vertex type, IDs, and optional vector attribute to get results.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
Matching MCP Servers
- AlicenseCqualityBmaintenanceA local-only codebase indexer that extracts syntax-aware chunks, creates embeddings locally, and enables semantic code search and symbol lookup via MCP.9MIT
- FlicenseNot gradedqualityDmaintenanceEnables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.-
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Operate your Vector Panda collections: search and upsert vectors, schema, measured indexes, exports.
- Initialize the local knowledge base by creating the SQLite database, vector index, and required directories. Run once to enable all retrieval tools and fix missing database errors.MIT
- Search Qdrant collections by vector similarity, passing a query vector or combining multiple prefetch queries with RRF/DBSF fusion for hybrid retrieval.MIT
- Analyze git diff between two refs to map changed files to wiki pages and code entities, or answer questions about the diff using RAG.MIT
- Splits clean text into overlapping chunks with unique IDs for vector embedding or search indexing.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
- Create vector embeddings to enable semantic code search across 55 languages and 99 file extensions. Run once per project before searching, then use incremental updates for changes.MITDestructive
- Verify health of embedding provider, Qdrant vector store, and Memgraph to confirm the memory backend is reachable before issuing recall queries.Apache 2.0
- Check the current index status, including database location, indexing state, total items, modules indexed, and progress if running.MIT
- Register an absolute project directory for semantic indexing; creates the Qdrant collection and starts an initial background index, so you can search code by meaning.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
- Remove a project's indexed codebase from the vector database, safely stopping file watchers, canceling pending indexing, and waiting for in-flight graph builds before deleting the index.AGPL 3.0
- 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
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT