How MCP + Semantic Layers Turn LLMs Into Trusted BI Analysts

Large Language Models (LLMs) are transforming analytics, but only when paired with the right foundation. In this episode, AtScale’s CTO and co-founder Dave Mariani, co-founder Dianne Wood, and product director Petar Staykov show how Model Context Protocol (MCP) and the semantic layer unlock trusted, governed, enterprise-ready AI.You’ll get a full end-to-end walkthrough of Claude using AtScale’s MCP server to:Discover available semantic models via list-models tools Understand metrics, attributes, and dimensional context using describe-model Run fully governed analytical queries, without writing SQL, through run-queryGenerate insights, summarize trends, and even build dashboards autonomously Learn why LLMs struggle without structured business logic and how semantic layers eliminate hallucinations and governance drift. You’ll also learn how AI will soon help build semantic models, not just query them—enabling faster metric definitions, autonomous metadata generation, and an “army of agents” that maintain semantic consistency as businesses evolve. Key topics in this episode:• Why MCP is the JDBC-moment for LLMs• How semantic layers make AI deterministic, trustworthy, and governed• Real demos of LLM insights far beyond simple Q&A• Automating semantic model creation using agents and SML• The future: AI copilots for BI, analytics, and semantic modeling

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