AI models are becoming a commodity. GPT-4, Claude, Gemini. Pick one, swap it next quarter, and the differentiation has already moved on. What hasn't moved is context: the data an agent can actually reach. Most of today's MCP ecosystem wraps the easy 80 percent. SaaS APIs, ticketing systems, chat platforms, anything that already had a REST endpoint. The other 20 percent: legacy databases running since the 1980s, CAD/BIM/GIS formats, real-time sensor and SCADA feeds, regulated records that legally can't leave the building, and hybrid environments split across cloud and on-prem by design, still have no real path to an agent. In most enterprises, that's exactly where the decision-relevant data lives. Drawing on 32 years building spatial and enterprise data integration, this talk looks at what it actually takes to expose hard, hybrid, and on-prem data as MCP tools: treating data workflows as callable tools instead of one-off scripts, separating the control plane (what an agent is allowed to call) from execution (where the data actually lives and stays), and building both directions, consuming MCP tools and exposing your own, without hardwiring to one model or vendor.
