You've heard about AI agents. You've seen ChatGPT, Gemini, and other AI tools. But here's what you don't realize: AI agents are about to get real access to your business.
On December 10, 2025, Google announced something that changes everything: official Model Context Protocol (MCP) support for all Google and Google Cloud services.
Think of MCP as "USB-C for AI"—a universal standard that lets AI agents connect to your data, your infrastructure, and your tools. Before this, connecting AI to your business systems was complicated, fragile, and risky. Now, Google has made it enterprise-ready and globally available.
This isn't just a technical update. This is the foundation for AI agents that can actually do things—manage your infrastructure, query your data, optimize your systems, and solve real business problems autonomously.
What Is MCP and Why Does It Matter?
Model Context Protocol (MCP) is a standard created by Anthropic that allows AI models to connect to external data sources and tools. It's like giving AI a standardized way to plug into your entire technology stack.
Before MCP, if you wanted an AI agent to work with your data or systems, you had to:
- Build custom integrations for each service
- Manage local MCP servers yourself
- Deal with fragile, hard-to-maintain connections
- Accept security risks from moving data into AI context windows
- Handle complex authentication and access control manually
With Google's new managed MCP servers, all of that complexity disappears. You get:
- Fully-managed, remote servers—no installation or maintenance
- Enterprise-grade security—data stays in place, governed by Google Cloud IAM
- Unified access—one standard way to connect AI to all Google services
- Global consistency—same endpoints everywhere
- Built-in observability—audit logging and monitoring
What Google Services Now Support MCP?
Google is rolling out MCP support incrementally, starting with these critical services:
1. Google Maps: Grounding AI in the Real World
What it does: Connects AI agents to real-world location data, weather forecasts, and routing information.
Why it matters: AI agents can now answer location-based questions accurately without hallucinating. For example, an AI assistant can tell you "How far is the nearest park from this rental?" or "What should I pack for the weather in Los Angeles this weekend?" using real, up-to-date data.
Business impact: Location-based services, real estate, travel, and logistics businesses can now build AI agents that provide accurate, real-world information to customers.
2. BigQuery: Reasoning Over Enterprise Data
What it does: Allows AI agents to interpret database schemas and execute queries against your enterprise data without moving data into AI context windows.
Why it matters: Your data stays secure and in-place. AI can analyze your business data, run forecasts, and generate insights without the security risk or latency of copying data.
Business impact: AI agents can now directly analyze your sales data, customer behavior, inventory, and financial metrics. They can answer questions like "What's our revenue forecast for Q1?" or "Which products are underperforming?" using your actual data.
3. Google Compute Engine (GCE): Autonomous Infrastructure Management
What it does: Exposes infrastructure capabilities (provisioning, resizing, management) as discoverable tools for AI agents.
Why it matters: AI agents can autonomously manage your cloud infrastructure—from initial builds to day-2 operations. They can dynamically adapt to workload demands, provision resources, and optimize costs.
Business impact: Infrastructure management becomes autonomous. AI agents can handle scaling, resource optimization, and infrastructure changes based on actual demand, reducing costs and improving performance.
4. Google Kubernetes Engine (GKE): Autonomous Container Operations
What it does: Provides a structured interface for AI agents to interact with both GKE and Kubernetes APIs reliably.
Why it matters: No more parsing brittle text output or stringing together complex CLI commands. AI agents can diagnose issues, remediate failures, and optimize container deployments autonomously.
Business impact: Containerized applications can be managed, optimized, and fixed by AI agents. This means faster incident response, better resource utilization, and reduced operational overhead.
Coming Soon: More Services
Google is rolling out MCP support for additional services in the coming months:
- Compute & Storage: Cloud Run, Cloud Storage, Cloud Resource Manager
- Databases & Analytics: AlloyDB, Cloud SQL, Spanner, Looker, Pub/Sub, Dataplex Universal Catalog
- Security: Google Security Operations (SecOps)
- Cloud Operations: Cloud Logging, Cloud Monitoring
- Google Services: Developer Knowledge API, Android Management API
This means AI agents will soon have access to your entire Google Cloud stack—databases, storage, security, monitoring, and more.
The Enterprise Advantage: Apigee Integration
Here's where it gets powerful for enterprises: Google is extending MCP capabilities through Apigee, allowing you to expose your own APIs as discoverable tools for AI agents.
This means:
- Your custom APIs become tools that AI agents can use
- Third-party APIs can be integrated and exposed to agents
- Business logic in your APIs becomes accessible to AI
- Governance and control through Apigee's API management
Your entire application stack—from containers to relational databases to custom business logic—becomes accessible to AI agents through a unified, governed interface.
Built-in Security and Observability
Google isn't just making MCP available—they're making it secure and observable:
- Cloud API Registry & Apigee API Hub: Discover trusted MCP tools from Google and your organization
- Google Cloud IAM: Manage access control through your existing identity system
- Audit Logging: Full observability into what AI agents are doing
- Google Cloud Model Armor: Defense against advanced agentic threats like indirect prompt injection
This isn't just about connecting AI to your systems—it's about doing it securely, with full visibility and control.
Real-World Example: Retail Location Intelligence
Google's announcement includes a compelling example: an AI agent that helps identify ideal retail locations.
The agent uses:
- BigQuery to forecast revenue based on sales data
- Google Maps to scout for complementary businesses and validate delivery routes
- All via standard, managed MCP servers—no custom code required
This is the future: AI agents that combine multiple data sources and services to solve complex business problems, all through standardized, managed connections.
What This Means for Your Business
For Website Owners
AI agents can now directly interact with your website infrastructure, databases, and analytics. They can optimize performance, analyze traffic patterns, and manage resources autonomously.
For Developers
Building AI-powered features becomes dramatically simpler. Instead of building custom integrations, you can use standard MCP connections to Google services. Your APIs can become tools that AI agents can discover and use.
For Business Owners
AI agents can now work with your actual business data and systems. They can answer questions about your business, optimize operations, and automate tasks using your real infrastructure and data.
For IT Teams
Infrastructure management becomes more autonomous. AI agents can handle routine operations, scaling, and optimization, freeing your team to focus on strategic work.
The Bottom Line
Google's MCP support announcement represents a fundamental shift in how AI agents interact with business systems. It's moving from:
- Fragile custom integrations → Standardized, managed connections
- Local, hard-to-maintain servers → Global, enterprise-ready endpoints
- Security risks from data movement → Data stays in-place, governed
- Complex, brittle implementations → Simple, discoverable tools
As a founding member of the Agentic AI Foundation, Google is committed to evolving MCP through the open source community. This isn't a proprietary lock-in—it's a standard that will work across platforms and services.
The future of AI agents is here. They're no longer just chatbots that answer questions. They're becoming autonomous systems that can work with your data, manage your infrastructure, and solve real business problems.
If you're using Google Cloud services, this capability is now available. If you're not, this might be the moment to consider what AI agents could do with access to your entire technology stack.
Next Steps
- Learn more: Check out Google's MCP announcement and MCP documentation
- Explore the demo: See the complete code for Google's retail location intelligence example
- Plan your integration: Consider which Google services you use and how AI agents could enhance them
- Evaluate your APIs: Think about which of your APIs could become discoverable tools for AI agents
The age of agentic AI is here. The question isn't whether AI agents will work with your business systems—it's how quickly you can leverage this capability to gain a competitive advantage.
