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

Enables fast, targeted queries against an Obsidian vault knowledge graph using tools like search, neighbor traversal, and pathfinding, without needing to load the full graph into LLM context.

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.

A knowledge graph MCP server providing persistent, structured memory for AI assistants with multi-agent isolation, tiered search, and index navigation.

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.

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.

An MCP server for tracking student learning via a knowledge graph, with spaced repetition scheduling and intelligent queries to identify gaps, misconceptions, and optimal next steps.

Connects to an embedded Kuzu graph database and provides read-only MCP tools for AI clients to query a knowledge graph.

RAG Knowledge Graph - MCP server providing AI-powered tools and automation by MEOK AI Labs

An embedded knowledge graph in one SQLite file, exposing a MCP server for agents to read and write entities and relationships with full audit trail and time travel.

MCP server for querying versioned C2PA specification knowledge graphs, enabling AI agents to browse entity definitions, validation rules, and version diffs via natural language.

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.

Enables AI agents to inspect ontology schemas, search typed objects, and execute read-only SPARQL queries against an RDF triple store.

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.

Reference implementation of the Operational Ontology pattern: MCP tools are generated from a typed business domain model (objects, links, actions) — one tool per query shape and per action, deliberately no raw SQL tool. Writes pass business-rule preconditions, are audited, and write back to the systems of record; refusals are machine-readable. Ships with a demo orders ontology spanning two legacy

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Provides LLM clients with a persistent, scalable knowledge graph memory system that supports semantic retrieval, contextual recall, and temporal awareness.

A lean, local knowledge graph that joins a repo's code to its aSPARK delivery artifacts, enabling agents to trace user stories to code and assess impact of changes, served over MCP.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

An MCP server that gives your agent a persistent project brain: vision, architecture decisions, conventions, roadmaps, and automatic session handoff.

An MCP server that uses an OWL/SHACL/SKOS knowledge graph to interpret login-diagnosis prompts and return JSON-LD descriptors, guiding Copilot agents to the appropriate SQL Server and MongoDB queries and enabling New Relic escalation when needed.