Google is rolling out fully managed MCP servers designed to give AI agents clean, reliable access to its services without the messy integration work developers typically face. The launch follows the debut of the company’s new Gemini 3 model, and it signals Google’s push to make its ecosystem “agent-ready by design,” pairing stronger reasoning models with deeper, real-world connectivity.
Until now, developers building AI agents have had to stitch together brittle connectors for tools like Maps, BigQuery or Kubernetes. Those setups are difficult to scale and create governance gaps. Google’s managed MCP servers tackle that by offering ready-made, remote endpoints that agents can call directly. Instead of spending days wiring up integrations, developers can simply drop in a URL.
The first set of MCP servers covers Maps, BigQuery, Compute Engine and Kubernetes Engine. In practice, that means an analytics assistant could query BigQuery for live data, or an operations agent could manage infrastructure tasks. For Maps, the shift is especially important: rather than relying on a model’s static knowledge, agents can retrieve fresh, ground-truth location data for trip planning or local queries.
The servers are launching in public preview and will be free for enterprise customers already paying for Google Cloud services. General availability is expected early next year, with additional MCP servers rolling out weekly. Google plans broader support across storage, databases, logging, monitoring and security.
MCP, Model Context Protocol, was created by Anthropic and has become the dominant open standard for connecting AI systems to tools and data sources. Because MCP is interoperable, Google’s servers can be used by clients from multiple providers. Google has already tested them with Gemini CLI, AI Studio, Anthropic’s Claude and OpenAI’s ChatGPT.
A major part of Google’s enterprise pitch is integration with Apigee, its API management platform. Apigee can effectively convert existing APIs into MCP-compatible endpoints, allowing companies to expose internal systems to AI agents using the same keys, quotas, monitoring and governance controls already in place for human developers. That means an internal product catalogue API, for example, can instantly become a tool an AI agent can discover and use.
Google’s new MCP servers also come with built-in security layers, including Identity and Access Management controls to limit what an agent can do, Model Armor to defend against threats like prompt injection and data exfiltration, and audit logging for visibility.
The strategy is clear: remove the integration and compliance burden so enterprises can deploy AI agents at scale without rebuilding their infrastructure from scratch. Or as Google puts it, “We built the plumbing so developers don’t have to.”
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