Google has introduced Model Context Protocol (MCP) support for its services, which aims to enhance AI applications by connecting them with data and tools more effectively. MCP, described as a "USB-C for AI," allows AI models to perform complex, multi-step tasks necessary for solving real-world problems. Previously, developers faced challenges with local MCP servers, which often led to fragile implementations. The new fully-managed, remote MCP servers simplify this process by enabling developers to point their AI agents or MCP clients to a stable endpoint for Google services. The rollout of MCP starts with specific services like Google Maps, BigQuery, Google Compute Engine, and Google Kubernetes Engine. For instance, Google Maps Grounding Lite allows AI agents to provide accurate location-based answers, while BigQuery facilitates reasoning over enterprise data without compromising security. The Google Compute Engine MCP server helps agents manage infrastructure autonomously, and the Google Kubernetes Engine supports reliable container operations through a structured interface. Google is also enhancing security and governance with tools like the Cloud API Registry and Apigee API Hub. These allow developers to discover trusted MCP tools and manage access effectively. The company plans to expand MCP support further, targeting services across projects, databases, security, and more. This effort is part of Google's commitment to advancing the AI ecosystem, ensuring that developers can focus on innovation while their agents work seamlessly with a wide range of tools and data sources.