What does "governed data" actually look like in practice? Here's @FactSet's MCP server connected inside @Databricks Playground: 13 tools exposed to the model, covering fundamentals, estimates, ownership, supply chain, M&A, RBICS, calendar events, and more, plus a parallel multi-tool wrapper for running several lookups in one pass. The model doesn't have to guess which dataset holds an answer or scrape a page and hope the numbers are current. It calls a defined tool, gets a structured, point-in-time response, and that response is traceable back to its source. This is the layer that sits between "agent framework" and "trustworthy output": a governed set of callable tools, not a general-purpose crawler. Curious how this fits into the rest of the stack? Our latest blog breaks it down: