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

A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.

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 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.

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

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.

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.

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.

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.

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 ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.

Combines a bi-temporal knowledge graph with dynamic automation tool generation for AI agents, enabling persistent memory with temporal tracking and custom tool creation.

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 containerized FastAPI + MCP server that lets LLM agents inject structured entities and relationships into a Neo4j graph database with safe Cypher execution.

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.

MCP server for querying and analyzing TensorBoard event files, enabling AI agents to extract scalar data, statistics, and correlations without a running TensorBoard server.

Enables AI assistants to query the Ubergraph biomedical ontology SPARQL endpoint with tools for custom SPARQL queries, term lookup, search, and hierarchy traversal.

Enables searching and querying Korean law ontology via SPARQL, with tools for law search, detail, agency, region, statistics, and network visualization.