Search for entities in your knowledge graph memory using semantic meaning and vector similarity to find relevant information based on conceptual relationships.
MIT
101,581 tools. Last updated 2026-04-10 00:09
- Retrieve vector embeddings for entities stored in a knowledge graph memory system to enable semantic search and analysis.MIT
- Search a knowledge base using semantic queries to find relevant information based on meaning rather than exact keywords.
- Check the current status of the knowledge base vector store to monitor build progress, document count, and cache statistics.
- Search a knowledge base using semantic queries to find relevant information from stored documents.
- Search Fodda's expert-curated knowledge graphs using hybrid vector and keyword methods to find relevant trends and articles across industries like retail, beauty, and sports.
Matching MCP Servers
- AsecurityAlicense-qualityAn improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.Last updated 4 months ago9439838MIT
- AsecurityAlicense-qualityEnables creating, managing, analyzing, and visualizing knowledge graphs with support for multiple graph types (topology, timelines, changelogs, requirements, knowledge bases, ontologies) including node/edge management and resource association.Last updated a year ago15441MIT
Matching MCP Connectors
AI-powered knowledge base for Double - Thank You with semantic search and question answering.
Make your knowledge agent-ready. Connect docs from Confluence, Notion, GitHub, Dropbox, or Google Drive — any AI agent searches them via one MCP endpoint. 3 retrieval modes: vector search, broad search, and full document access. The agent decides how deep to dig.