"Learning NestJS Framework Knowledge" matching MCP servers:

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.

Analyzes pommel horse gymnastics routines using a categorical aesthetics framework that evaluates element groups, spatial patterns, temporal qualities, and form with 85% deterministic taxonomy and 15% LLM synthesis.

Exposes the canonical 7-0 knowledge surface — game modes, roster picks, and scenarios, FAQ, official links

Provides game rules, roster constraints, FAQ, and official links for the 27-0 NRL fantasy draft game, enabling AI clients to retrieve this knowledge.

Exposes the canonical 17-0 knowledge surface including game rules, roster constraints, and entry points for the NFL roster strategy game to MCP-compatible AI clients.

Enables AI agents to safely interact with golf course operations by providing real-time inventory, booking rules, pricing policies, and a self-learning memory system.

Provides access to a cited knowledge graph on football's Laws of the Game and major competitions, with tools for search, question answering, and entity exploration, returning claims with sources and confidence.