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A BigQuery MCP server lets AI agents like Claude query your Google BigQuery data through a standard protocol instead of ad-hoc integrations. Because BigQuery bills by bytes scanned, an unconstrained agent is not just a security risk but a budget risk: one careless full-table scan on a wide table costs real money. This guide covers what a BigQuery MCP server does, the three ways to set one up, and the cost and security controls that matter before you let an agent anywhere near your analytics data.

Every utility is being pulled in two directions. Operations, engineering, and customer-facing teams all want data in modern applications: outage maps, field-service apps, asset-health dashboards, regulator reporting, and increasingly AI assistants that can answer questions about generation, load, or maintenance history. At the same time, the security team's job is to make sure none of that convenience becomes a path into systems that open spillway gates, trip breakers, or control pressure.

Copying a PropTech feature set used to take real time and money. It doesn’t anymore, and that changes what makes a product defensible. Property management systems and point solutions are converging on the same capabilities, and the overlap between EliseAI and Funnel earlier this year showed how quickly two platforms in separate lanes can end up competing head-on.

You've mapped the architecture. You know your agents need to retrieve context from external tools, coordinate with other agents, and propagate mutations through your systems. The model logic is solid. What you haven't fully solved is what sits between those agents and everything they're trying to reach. That's the gap Kong was built to close. Multi-agent workflows live and die on context.