Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.
MIT
466,320 tools. Updated 2026-08-19 13:05
"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
- Search code by meaning and structure using hybrid vector and graph queries to locate relevant files, signatures, and dependencies.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
Matching MCP Servers
- Flicense-qualityDmaintenanceEnables 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.
- AlicenseCqualityBmaintenanceA local-only codebase indexer that extracts syntax-aware chunks, creates embeddings locally, and enables semantic code search and symbol lookup via MCP.9MIT
Matching MCP Connectors
Corporate travel: search and book flights, hotels, rail and transfers, manage orders.
Cloudflare Workers MCP server: code-explainer
- Check health of all memory components: embedding provider, Qdrant vector store, and optional Memgraph graph database. Returns JSON status to verify backend connectivity before recall queries.Apache 2.0
- Indexes a codebase to build vector embeddings for semantic code search across 50+ languages. Required initial step before searching code.MIT
- Check the current index status, including database location, indexing state, total items, modules indexed, and progress if running.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 codebase index from the vector database, safely stopping file watchers and canceling in-progress updates before deletion.AGPL 3.0
- Save research content to a vector database for semantic search and future retrieval, organized by topic for easy access.MIT
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT
- Ask questions about recent code changes and get answers synthesized from git diff and code context. Understand what changed, identify bugs, or assess impact without manual code review.MIT
- Retrieve integration documentation for databases, vector stores, and models. Specify the integration type and optional name to get targeted guides.MIT
- Set up a ChromaDB vector database with LangChain integration, storing text embeddings and optional metadata, with persistence support, returning a retriever.MIT
- Analyze source code complexity with AST-based metrics including cyclomatic complexity, nesting depth, and function counts. No prior indexing needed.MIT
- Query a ChromaDB vector database to retrieve relevant documents using LangChain integration. Specify the persist directory and number of results.MIT
- Retrieve relevant information from a long-term knowledge base using semantic search. Access context, facts, or past interactions stored in Qdrant.MIT