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Now live on GitHub, gcloud-mcp is the open-source reference implementation bringing agentic power to Google Cloud via the Model Context Protocol. As cloud platforms grow more complex, traditional CLIs struggle to support exploratory, goal-driven interactions. This session shares architectural lessons from building the bridge between AI assistants and enterprise cloud services to enable agent-driven reasoning.
Using the Storage MCP server as a deep dive, we demonstrate how AI assistants interact with Google Cloud Storage via MCP. We’ll show how raw GCS APIs were transformed into high-level Storage Intelligence tools that support meaningful workflows rather than simple command execution.
We focus on the design of the /storage-mcp package, including how we summarize GCS metadata into concise, accurate responses that agents can reason over without hitting context limits. Finally, we discuss how metrics like cost, latency, and task accuracy guided our iteration, helping refine prompt design and tool granularity. Attendees will leave with practical, enterprise-ready patterns for building local MCP servers that enable efficient interaction with complex cloud infrastructure.
