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

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.

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

Enables searching Google's Knowledge Graph for real-world entities via two tools: search by query or lookup by Machine ID, returning structured data including entity types, descriptions, and URLs.

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.

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.

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

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

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.

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

Enables intelligent exploration of Unreal Engine documentation through a Neo4j-based knowledge graph. Supports concept relationship searches, learning path discovery, and dependency tracking between engine features using bilingual Chinese-English queries.

A semantic ontology layer for Pendo that enables users to define and manage business concepts, entity relationships, and measurement hints, grounding LLM queries in workspace-specific definitions.

Semantic React/TSX analysis MCP server that saves 70-90% tokens by using AST to retrieve precise component usages, prop flow, and state update information for AI coding assistants.

An MCP server for Dart AI task management that enables bulk operations using DartQL selectors to minimize token usage and context rot. It provides tools for batch updates, task and document CRUD, and safe CSV imports with integrated dry-run capabilities.

Self-hosted personal knowledge graph for Claude that persists across sessions, devices, and tools. Built on Neo4j with local semantic embeddings; OAuth 2.1 lets Claude Code, Claude Desktop, and claude.ai web all hit the same graph.

An MCP server that guides AI assistants to navigate documentation using their built-in tools (grep, file reading) instead of traditional RAG.

Enables read-only querying of Azure Log Analytics and Azure Resource Graph through MCP, supporting KQL queries, workspace discovery, and resource inventory exploration with Azure RBAC authentication.

Enables cross-session memory and recall for AI coding assistants through a git-synced knowledge base, hybrid semantic and TF-IDF search across tool transcripts, and automatic distillation of insights with secrets scrubbing.

A Model Context Protocol server for querying large JSON files using JSONPath expressions, enabling LLMs to efficiently search and extract information from large JSON data.