Initialize a new vector collection at a specified path, define its vector and scalar fields with optional indexes, and open it for use.
Apache 2.0
619,831 tools. Updated 2026-09-28 18:53
"How to work with a vector database" matching MCP tools:
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.Apache 2.0
- Remove a vector index from a table in Baidu Vector Database to manage storage or modify search structures.Apache 2.0
- Check the local CrossRef database status: path, work count (~167M), FTS5 index size, citation-graph edge count, and access mode. Use it to confirm the database is ready before running searches.AGPL 3.0
- Discover supported vector database providers and their required and optional config fields, along with setup notes, to prepare for syncing data.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
Matching MCP Servers
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Matching MCP Connectors
Push rows from a dataset, file or Google Sheet into a Postgres, Supabase, Neon or MySQL table.
MobilityData's open catalogue of public-transport feeds — ~4,500 GTFS schedule feeds and ~1,900…
- 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
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT
- Set up a ChromaDB vector database with LangChain integration, storing text embeddings and optional metadata, with persistence support, returning a retriever.MIT
- Query a ChromaDB vector database to retrieve relevant documents using LangChain integration. Specify the persist directory and number of results.MIT
- Save research content to a vector database for future retrieval. Use topics to organize data into separate collections for easier management.MIT
- Add documents to the Azure AI Search vector database by generating embeddings for content, enabling future semantic searches across project knowledge.MIT
- Search a vector database with natural language queries to retrieve the most semantically similar documents from an Azure AI Search index, using generated query embeddings to find relevant results.MIT
- List all documents stored in the vector database with pagination. Set limit and offset to navigate through results.MIT
- Store information in a vector database for later retrieval. This tool adds context entries with unique IDs, content, and optional metadata to enable semantic search capabilities.MIT
- Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.MIT
- 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
- Re-embed all memories in the vector database to refresh and update stored information for accurate retrieval.MIT
- Retrieve vector database statistics including stored context count and dimensions to monitor data volume and structure.MIT
- Convert skill directories into Weaviate-ready format for hybrid vector and keyword search. Enables production RAG applications with BM25 and vector retrieval.MIT