Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.
Apache 2.0
161,150 tools. Last updated 2026-05-29 23:24
"How to work with a vector database" matching MCP tools:
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT
- Retrieve a paginated list of all documents stored in the Chroma vector database, specifying limit and offset for efficient navigation and management.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
- Retrieve vector database statistics including stored context count and dimensions to monitor data volume and structure.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
Matching MCP Servers
- FlicenseAqualityCmaintenanceAn MCP server that converts Markdown files containing Mermaid diagrams into PDF documents by rendering diagrams as SVG images. It provides a specialized tool to automate document conversion while ensuring all visual charts are correctly embedded in the final output.Last updated1
- AlicenseBqualityCmaintenanceAn AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.Last updated51Apache 2.0
Matching MCP Connectors
Korean business record validation and workflow safety gates for AI agents.
Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.
- Re-embed all memories in the vector database to refresh and update stored information for accurate retrieval.MIT
- Export your skill directory to Chroma vector database format, enabling local-first storage for AI-ready skills and RAG knowledge.MIT
- Retrieve vector database configuration details to understand storage setup and manage embeddings for AI applications.MIT
- Index a local project directory into a vector database to enable semantic code search and analysis across your codebase.MIT
- Retrieve a specific document from the Chroma vector database using its unique ID, enabling efficient document access and management for semantic search and metadata filtering.MIT
- Modify and update content or metadata of an existing document in the Chroma vector database using a specified document ID.MIT
- Generate and store new documents in Chroma's vector database with unique IDs, content, and metadata, enabling efficient semantic search and document management.MIT
- Modify vector database settings to change storage providers or adjust configuration parameters for AI data management.MIT
- Export skills to Qdrant vector database format for high-performance search and native payload filtering, enabling AI-ready RAG knowledge.MIT
- Store content in a vector database for semantic search and retrieval. Save text with unique identifiers to enable AI-powered similarity-based queries.
- Retrieve the k nearest nodes to a query embedding using HNSW vector similarity. Use for semantic search after creating a vector index on a specific label and property.Apache 2.0
- Performs semantic vector search, then expands each result's graph neighborhood to reveal contextual relationships.Apache 2.0
- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- Build and preview a database transfer command: displays the CLI with masked passwords and auto-suggests a parallelism method for the source database.MIT