"How to Query a Knowledge Graph Using an Ontology" matching MCP servers:

• mcp-knowledge-graph

  shaneholloman

  A

  security

  A

  license

  -

  quality

  An 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 ago

  9

  439

  838

  MIT

• How-To-Cook

  xiaobenyang-com

  A

  security

  A

  license

  -

  quality

  An 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 updated 3 months ago

  5

  1

  Apache 2.0

• Knowledge Graph Memory Server

  itseasy21

  A

  security

  A

  license

  -

  quality

  An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.

  Last updated 8 months ago

  11

  657

  60

  JavaScript

  MIT

• Knowledge Graph Memory Server

  deanacus

  -

  security

  F

  license

  -

  quality

  A persistent memory server that implements a local knowledge graph using the Kuzu embedded database to store entities, relationships, and observations. It enables AI models to maintain structured long-term context through searchable nodes and comprehensive tag-based organization.

  Last updated 10 months ago

  4

• Knowledge Graph MCP Server

  aiuluna

  A

  security

  A

  license

  -

  quality

  Enables 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 ago

  15

  44

  1

  MIT

• Cardiology Knowledge Graph MCP

  ryoureddy

  -

  security

  F

  license

  -

  quality

  Builds and manages a cardiology-focused knowledge graph in Neo4j by extracting medical entities and relationships from documents using LLMs. It enables users to ingest PDFs, refine extracted data, and perform natural language queries to gain clinical insights.

  Last updated 9 months ago