"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.

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.

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.

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

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

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

This MCP server builds a local flakiness knowledge graph from Playwright test run history and enables AI agents to query flaky tests, failure patterns, trends, and correlated git commits, helping diagnose test reliability without manual analysis.

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

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.

Enables LLM agents to retrieve structured diagnosis plans for login issues from an OWL/SHACL/SKOS knowledge graph, returning required entities, validation sequences, and New Relic escalation details.

Enables natural language querying of a football knowledge graph via Neo4j, generating Cypher queries and answers using LLMs, and also provides tools to build the graph from text.

A TypeScript MCP server for managing persistent knowledge graphs with entities, directional relations, and time-based observations. It enables users to create, search, and track structured information with built-in concurrency control and JSON file storage.

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

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.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Provides offline access to the Brick Schema ontology, enabling validation and search of building metadata classes through MCP tools.

Enables AI agents to read and optionally write to PostgreSQL databases through a multi-project setup with schema inspection, query execution, and performance analysis tools.

A local MCP server for secure database querying from Claude Code, with Apple Keychain credential storage, permission-based access control, and audit logging.