Skip to main content
Glama
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

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.

  • Export your skill directory to Chroma vector database format, enabling local-first storage for AI-ready skills and RAG knowledge.
    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
  • Generate and store new documents in Chroma's vector database with unique IDs, content, and metadata, enabling efficient semantic search and document management.
    MIT
  • Export skills to Qdrant vector database format for high-performance search and native payload filtering, enabling AI-ready RAG knowledge.
    MIT
  • 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