Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.
Apache 2.0
750,862 tools. Updated 2026-11-06 11:15
"Vector Database" matching MCP tools:
- Switch between databases in Baidu Cloud Vector Database to manage different datasets for vector search operations.Apache 2.0
- Retrieve all table names from the Baidu Vector Database to view available data collections for vector search operations.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
- Create a new vector database in Baidu Cloud to store and organize vector embeddings for AI applications.Apache 2.0
- Rebuild a vector index in Baidu Vector Database to optimize search performance and maintain data integrity for AI applications.Apache 2.0
Matching MCP Servers
- -licenseNot gradedqualityNot gradedmaintenanceTurn SEC EDGAR filings into a searchable vector database, enabling natural language queries over company filings through Claude Desktop.-
Baidu Vector Database MCPofficial
AlicenseBqualityDmaintenanceA server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.143Apache 2.0
Matching MCP Connectors
Database schema inference, dialect normalization, query plan optimization, and SQL audit over streamable HTTP.
MobilityData's open catalogue of public-transport feeds — ~4,500 GTFS schedule feeds and ~1,900…
- Remove a vector index from a table in Baidu Vector Database to manage storage or modify search structures.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
- Discover all vector columns in your PostgreSQL database, including table names and vector dimensions. Optionally filter by schema.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
- List Cloudflare Vectorize vector database indexes with dimensions, metric, creation timestamp, and description.MIT
- List all documents stored in the vector database with pagination. Set limit and offset to navigate through results.MIT
- Starts the Qdrant vector database container for vector search functionality in Hephaestus development, avoiding manual Docker setup.MIT
- 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
- Delete a document from the Chroma vector database by its unique ID. Remove outdated or incorrect entries to maintain accurate data.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
- Retrieve a document from a vector database by providing its unique ID.MIT
- Add a document with content and metadata to the Chroma vector database using a unique document ID.MIT