Google Launches Official MCP Support: Unlocking the Full Potential of AI Agents Across Services

The Evolution of AI: From Intelligent Models to Action-Oriented Agents

Artificial intelligence has undergone remarkable transformation in recent years. With the introduction of advanced reasoning models like Gemini 3, we now possess unprecedented capabilities to learn, build, and plan. These sophisticated AI systems can process complex information and generate insightful responses. Yet a fundamental question remains: what truly transforms an intelligent model into a practical agent that can solve real-world problems on our behalf?

The answer lies not just in raw intelligence, but in the ability to interact reliably with tools and data. An AI system might understand your request perfectly, but without access to current information and actionable capabilities, it remains limited to theoretical responses. Imagine asking about weekend weather in Los Angeles or the distance to the nearest park from your rental property—without direct access to current data sources, even the most advanced AI might provide outdated or entirely fabricated answers. This gap between understanding and action has been a significant barrier to creating truly helpful AI assistants.

For AI to evolve beyond conversation partners into effective agents that pursue goals and solve tangible problems, they need consistent, secure pathways to the digital tools and information sources that power our world. They need to interact with mapping services, analyze business data, manage infrastructure, and access countless other capabilities—all while maintaining security, accuracy, and reliability. Until recently, creating these connections required complex, custom integrations that were difficult to maintain and often fragile.

Understanding MCP: The Universal Connector for AI Systems

Enter the Model Context Protocol (MCP), often described as the “USB-C for AI.” Developed through collaboration between industry leaders including Anthropic, MCP establishes a standardized way for AI models to connect with external tools and data sources. Just as USB-C simplified connectivity for physical devices, MCP creates a universal interface that allows AI systems to work seamlessly with diverse applications and services.

Before MCP gained traction, developers faced significant challenges when building agent capabilities. They needed to identify, install, and manage individual MCP servers—often running locally on their machines or through open-source deployments. This approach placed substantial burden on development teams, requiring specialized knowledge to maintain these connections. Worse, these implementations frequently proved fragile when scaled to production environments, breaking when services updated or when handling complex multi-step tasks.

The core innovation of MCP lies in its ability to enable complex workflows that mirror real-world problem-solving. Consider planning a family vacation: an effective AI agent might need to check weather forecasts, find child-friendly restaurants near your hotel, calculate travel times between destinations, and estimate costs—all while maintaining context across these different tasks. MCP provides the structured framework that makes these sophisticated interactions possible without overwhelming developers with integration complexity.

Google’s Managed MCP Solution: Enterprise-Ready Integration

Today marks a significant advancement in the agent ecosystem as Google announces fully-managed, remote MCP servers that integrate directly with Google and Google Cloud services. This development transforms how developers build AI agents by eliminating previous infrastructure headaches. Instead of managing individual servers, developers can now point their AI agents or standard MCP clients like the Gemini CLI to globally-consistent, enterprise-grade endpoints.

This new capability represents more than just convenience—it fundamentally changes the economics and feasibility of building production-ready AI agents. By leveraging Google’s existing API infrastructure enhanced with MCP support, organizations gain access to unified connections across the entire Google service ecosystem. This managed approach handles scaling, reliability, and updates automatically, freeing development teams to focus on creating valuable agent experiences rather than maintaining integration plumbing.

Perhaps most significantly, Google extends this MCP capability to broader enterprise environments through Apigee. This integration allows organizations to expose their own purpose-built APIs—whether developed internally or sourced from third parties—as discoverable tools for AI agents. Companies can now govern and secure their unique business logic and data flows while making them accessible to agent systems. This bridges the gap between specialized enterprise systems and the emerging agent ecosystem, enabling AI assistants that understand and work within specific organizational contexts.

Deep Dive: MCP-Powered Services Transforming Agent Capabilities

Google Maps: Grounding Agents in Physical Reality

One of the most challenging aspects of building useful AI agents has been connecting digital intelligence to the physical world. Without access to accurate location data, weather information, and routing details, agents often “hallucinate” answers to real-world queries. The new MCP integration with Google Maps Platform solves this problem through Maps Grounding Lite.

This capability provides AI agents with trusted geospatial data directly from Google’s mapping infrastructure. Agents can now answer questions like “How far is the nearest park from this rental property?” or “What should I pack for the weather in Los Angeles this weekend?” with confidence, drawing from fresh, authoritative sources. For business applications, agents can evaluate location suitability by analyzing foot traffic patterns, identifying complementary businesses nearby, or validating delivery route efficiency.

Imagine a real estate agent assistant that doesn’t just describe properties but can instantly calculate commute times to workplaces, identify nearby schools and parks, and alert potential buyers about neighborhood amenities—all through natural conversation. Or consider a travel planning agent that recommends restaurants based on your dietary preferences while considering current wait times, parking availability, and proximity to your hotel. These aren’t theoretical possibilities anymore; they’re practical applications enabled by Maps Grounding Lite’s MCP integration.

The significance extends beyond convenience. When agents provide location-based information, accuracy isn’t just helpful—it’s essential for trust and safety. By grounding responses in Google’s verified mapping data rather than relying on potentially outdated or incorrect information from training data, MCP-powered agents deliver reliable guidance that people can actually use in their daily lives.

BigQuery: Unlocking Enterprise Data for Intelligent Reasoning

Enterprise data represents one of the most valuable yet underutilized resources for AI agents. Until now, connecting agents to business data often involved risky practices like copying sensitive information into context windows or building fragile query interfaces. The BigQuery MCP server changes this paradigm by enabling agents to natively interpret database schemas and execute queries against enterprise data while keeping information secure and in-place.

This integration provides several critical advantages. First, agents can understand the structure and meaning of organizational data without requiring developers to build custom parsers for each dataset. Second, queries execute directly against BigQuery’s processing engine, eliminating the latency and security risks of moving large datasets. Third, agents gain access to BigQuery’s advanced capabilities like forecasting and machine learning directly within their workflows.

Consider a retail business analyst agent that can forecast next quarter’s sales by analyzing historical patterns while considering current promotions and seasonal trends. Or imagine a supply chain assistant that monitors inventory levels across warehouses, predicts potential shortages based on sales velocity, and automatically generates restocking recommendations—all by interacting with BigQuery through MCP. These agents don’t just report data; they synthesize insights and suggest actions while respecting data governance policies.

The security implications are profound. Traditional approaches often required extracting data from secure environments into less protected spaces where AI models could access it. With the BigQuery MCP server, sensitive information never leaves Google’s secure infrastructure. Administrators maintain full control through Google Cloud IAM permissions, audit logging tracks all data access, and the system enforces organizational governance policies automatically. This transforms enterprise data from a liability into an agent superpower.

Google Compute Engine: Autonomous Infrastructure Management

Infrastructure management represents another domain where AI agents can deliver tremendous value, but only if they can safely interact with cloud resources. The Google Compute Engine (GCE) MCP server transforms how agents handle infrastructure tasks by exposing capabilities like provisioning virtual machines and resizing resources as discoverable, governed tools.

This integration enables agents to manage complete infrastructure workflows—from initial environment setup to day-to-day operations. An infrastructure agent might automatically scale computing resources during peak traffic periods, optimize machine types based on workload patterns, or implement cost-saving measures during low-usage hours. These aren’t theoretical capabilities; they’re practical automations that reduce operational burden while improving system reliability.

Consider a development team launching a new application. Instead of manually configuring servers, monitoring performance, and adjusting resources, an AI agent could handle these tasks autonomously. It might provision appropriate machine types based on application requirements, implement auto-scaling policies, and even perform routine maintenance during off-peak hours. When unexpected traffic spikes occur, the agent could dynamically allocate additional resources before users experience slowdowns, then scale back down when demand subsides—all without human intervention.

The GCE MCP server ensures these powerful capabilities remain secure and controllable. Administrators define precisely which actions agents can perform through granular permissions. Every infrastructure change is logged and auditable. Organizations can implement human approval workflows for sensitive operations while automating routine tasks. This balance of automation and governance makes autonomous infrastructure management practical for enterprises of all sizes.

Google Kubernetes Engine: Simplifying Container Operations

Container orchestration through Kubernetes has revolutionized application deployment but introduced significant operational complexity. Managing clusters, diagnosing failures, and optimizing resource usage demands specialized expertise that many organizations struggle to maintain. The Google Kubernetes Engine (GKE) MCP server addresses this challenge by providing agents with a structured, discoverable interface to interact reliably with both GKE and Kubernetes APIs.

Traditional approaches to Kubernetes management often involve parsing complex CLI output or stringing together multiple commands—a process prone to errors when automated. The GKE MCP server eliminates these fragilities by presenting Kubernetes capabilities as well-defined tools that agents can understand and use safely. This enables agents to perform sophisticated operations like diagnosing pod failures, implementing auto-scaling policies, or optimizing cluster costs through node pooling strategies.

Imagine an e-commerce platform experiencing sudden traffic surges. A GKE-powered agent could automatically detect increased load, analyze which microservices need additional resources, and scale specific deployments without affecting the entire application. If costs become a concern, the agent might identify underutilized resources, recommend right-sizing strategies, or implement spot instances for non-critical workloads—all while maintaining application performance.

For troubleshooting scenarios, agents gain unprecedented capabilities. When an application experiences errors, an agent could trace issues through multiple microservices, examine relevant logs, check resource constraints, and even suggest or implement fixes like rolling back problematic deployments. This transforms Kubernetes from a complex system requiring specialized operators into a self-healing platform that maintains itself with minimal human oversight.

The GKE MCP integration maintains crucial safety boundaries. Agents operate within defined permissions boundaries, with sensitive actions requiring human approval. All operations are logged for auditability, and organizations can implement progressive automation where agents suggest actions for human confirmation before execution. This approach builds trust while gradually increasing automation levels as confidence grows.

Enterprise-Grade Security and Governance for Agent Ecosystems

As AI agents gain access to increasingly powerful capabilities and sensitive data, security and governance become non-negotiable requirements. Google addresses these concerns through integrated security features built directly into the MCP infrastructure. This unified approach to discovery and governance ensures that agent interactions remain secure, auditable, and compliant with organizational policies.

The Cloud API Registry and Apigee API Hub form the foundation of this governance framework. These tools allow developers to discover trusted MCP tools from Google and their own organizations through centralized catalogs. No longer must teams manually track which APIs are agent-ready or vet custom integrations—everything appears in organized, searchable registries with clear documentation and ownership information.

Access management leverages Google Cloud IAM’s proven capabilities to control precisely which agents can interact with specific services and data sources. Administrators define fine-grained permissions based on roles, projects, or even specific resources. An agent assisting with marketing analytics might access campaign performance data but be blocked from financial systems. Another agent managing infrastructure could resize compute resources but lack permission to delete production databases. This principle of least privilege applies consistently across all MCP-enabled services.

Audit logging provides comprehensive visibility into agent activities. Every tool invocation, data access, and configuration change generates detailed records showing which agent performed the action, what resources were affected, and when the operation occurred. These logs integrate with existing monitoring and compliance systems, allowing security teams to detect anomalies or investigate incidents efficiently. For regulated industries, this audit trail becomes essential for demonstrating compliance during external reviews.

Google Cloud Model Armor adds another critical protection layer against sophisticated threats targeting AI systems. Agents interacting with external data sources face risks like indirect prompt injection attacks where malicious inputs manipulate agent behavior. Model Armor employs advanced techniques to detect and neutralize these threats before they compromise agent integrity. This protection operates transparently in the background, requiring no special configuration from developers while providing enterprise-grade security.

Together, these capabilities create a secure foundation for agent deployment at scale. Organizations gain confidence that their agents operate within defined boundaries, access only authorized resources, and maintain complete auditability. This security posture isn’t an afterthought—it’s architected into the MCP infrastructure from the ground up, enabling responsible innovation with agent technologies.

Real-World Application: Building a Retail Location Scout Agent

To demonstrate the practical power of Google’s MCP integration, consider building an AI agent to identify ideal retail locations—a complex task requiring multiple data sources and analytical capabilities. Using the Agent Development Kit (ADK) with Gemini 3 Pro as the reasoning engine, this agent combines several MCP-enabled services into a cohesive workflow.

The agent begins by understanding the user’s requirements through natural conversation. A business owner might explain they’re looking for a new bakery location in a residential neighborhood with good foot traffic, minimal competition from similar establishments, and reasonable rent prices. The agent translates these requirements into specific analytical tasks across different systems.

First, the agent connects to BigQuery through MCP to analyze sales forecasting data. It examines historical performance of existing locations, correlates sales with demographic factors, and projects revenue potential for candidate areas. This analysis might reveal that neighborhoods with high concentrations of dual-income families without children show stronger sales for premium baked goods during weekday afternoons.

Simultaneously, the agent uses Google Maps Grounding Lite to evaluate physical locations. It identifies potential storefronts matching the size and zoning requirements, then analyzes surrounding businesses to avoid direct competitors while seeking complementary establishments like coffee shops or grocery stores. The agent calculates pedestrian traffic patterns by analyzing footpath data and public transportation stops, estimates delivery route efficiency to suppliers, and even checks parking availability for customers.

As the agent processes this information, it maintains context across these different data sources. When BigQuery indicates strong potential in a particular zip code, the agent instantly cross-references that area in Maps to verify physical viability. If Maps shows limited parking in an otherwise ideal location, the agent might adjust its BigQuery analysis to prioritize areas with better infrastructure. This fluid integration between enterprise data and real-world context enables nuanced recommendations impossible with isolated tools.

The agent presents findings through natural conversation, explaining why certain locations stand out. It might highlight a storefront near a new apartment complex with growing families, moderate rent prices, and proximity to complementary businesses—all while avoiding areas with multiple existing bakeries. Crucially, every recommendation cites its data sources, building trust through transparency. The business owner can drill into specific aspects, asking for more details about traffic patterns or projected revenue scenarios, with the agent dynamically fetching updated information through MCP connections.

This retail scout agent represents more than a technical demonstration—it showcases how MCP integration transforms theoretical AI capabilities into practical business tools. By connecting Gemini 3’s reasoning power to Google’s data and service ecosystem through standardized interfaces, developers create agents that solve complex, real-world problems with minimal custom integration code. The managed MCP infrastructure handles reliability, scaling, and security automatically, allowing teams to focus on agent behavior rather than infrastructure concerns.

The Expanding MCP Ecosystem: Future Service Integrations

Google’s commitment to the agent ecosystem extends far beyond the initial MCP integrations. Over the coming months, organizations will gain access to managed MCP servers across dozens of additional Google Cloud and Google services. This expansion transforms MCP from a promising standard into the foundational connectivity layer for enterprise AI.

Projects, Compute, and Storage services will bring agent capabilities to core infrastructure management. Cloud Run integration will enable agents to deploy and scale serverless applications automatically based on traffic patterns or business events. Cloud Storage MCP connections will allow agents to organize, analyze, and process unstructured data like images and documents without manual intervention. Cloud Resource Manager integration will provide agents with visibility into organizational hierarchies, enabling governance-aware actions across complex multi-project environments.

Databases and Analytics services represent another major expansion area. AlloyDB and Cloud SQL integrations will allow agents to interact with relational databases using natural language, generating complex queries while respecting schema constraints. Spanner’s global-scale database capabilities will become accessible to agents needing strongly consistent data across regions. Looker integration will enable agents to generate and explain business intelligence reports conversationally. Pub/Sub connectivity will let agents respond to real-time data streams, while Dataplex Universal Catalog will help agents discover and understand data assets across the organization.

Security operations gain significant enhancement through Google Security Operations (SecOps) MCP integration. Agents will assist security teams by analyzing threat intelligence feeds, correlating alert patterns, and suggesting remediation steps during incidents. They could automatically isolate compromised resources, update firewall rules based on emerging threats, or generate compliance reports—all while maintaining strict access controls and audit trails.

Cloud operations capabilities expand through Cloud Logging and Cloud Monitoring integrations. Agents will proactively identify performance anomalies, correlate logs across microservices to diagnose failures, and even predict resource exhaustion before users experience issues. A cloud operations agent might notice gradually increasing memory usage in a critical service, analyze historical patterns, and recommend configuration changes before the system reaches critical thresholds.

Google services integration brings enterprise-grade agent capabilities to productivity and device management. The Developer Knowledge API will allow agents to access internal documentation and best practices, accelerating developer onboarding and problem-solving. Android Management API integration will enable agents to enforce security policies across corporate devices, remotely wipe lost equipment, or deploy applications based on employee roles.

This comprehensive rollout strategy reflects Google’s vision for a unified agent ecosystem. Rather than treating MCP as a niche capability, Google is weaving it into the fabric of its entire service portfolio. Each new integration follows consistent design principles around security, observability, and ease of use, ensuring developers experience predictable patterns when connecting agents to any Google service. This consistency dramatically reduces the learning curve for building multi-service agents while increasing reliability across complex workflows.

The Strategic Importance of MCP Standardization

The significance of Google’s MCP investment extends beyond technical convenience—it represents a strategic commitment to open standards in the emerging agent ecosystem. As David Soria Parra, Co-creator of MCP and Member of Technical Staff at Anthropic, observes: “Google’s support for MCP across such a diverse range of products, combined with their close collaboration on the specification, will help more developers build agentic AI applications. As adoption grows among leading platforms, it brings us closer to agentic AI that works seamlessly across the tools and services people already use.”

This perspective highlights a crucial industry shift. Without standardization, the agent landscape risks fragmentation where each platform develops proprietary connection methods. Developers would face the same integration challenges that plagued early cloud adoption—having to rebuild connections for every new service or vendor. MCP solves this by establishing a common language for agent-tool interaction, similar to how HTTP standardized web communication.

Google’s role as a founding member of the Agentic AI Foundation further demonstrates this commitment to open collaboration. By contributing to the evolution of MCP through this foundation, Google ensures the protocol evolves based on real-world needs rather than vendor-specific requirements. This approach builds trust with developers who fear vendor lock-in and want assurance their agent investments will remain viable as the ecosystem matures.

The business implications are substantial. Companies investing in agent development need confidence that their work will interoperate across environments. A retail agent built using Google’s MCP servers should potentially work with other MCP-compatible services in the future. This portability protects development investments and encourages innovation without fear of obsolescence. For Google, embracing open standards ultimately grows the entire market for agent-capable AI systems, expanding opportunities for Google Cloud services.

This standardization also accelerates enterprise adoption. IT leaders responsible for governance and security can evaluate MCP as a single integration pattern rather than vetting dozens of custom approaches. Compliance frameworks can incorporate MCP security patterns consistently. Training programs can teach one methodology for agent connectivity. This reduction in complexity lowers barriers to entry for organizations exploring agent technologies.

The USB-C analogy proves particularly apt here. Just as USB-C simplified device connectivity by replacing numerous proprietary chargers and cables, MCP promises to replace fragmented agent integration methods with a single, reliable standard. This standardization doesn’t limit innovation—it redirects creative energy toward building valuable agent experiences rather than rebuilding connectivity infrastructure repeatedly.

Practical Implementation: Getting Started with Google’s MCP Services

Organizations ready to explore agent development with Google’s MCP services have clear pathways to begin. Google provides comprehensive documentation covering MCP concepts, service-specific implementations, and integration patterns. The MCP overview documentation serves as the central starting point, explaining core concepts and linking to specialized guides for each supported service.

For developers building Maps-integrated agents, the Google Maps Platform documentation details how to access Grounding Lite capabilities through MCP. This includes authentication methods, query parameters, and response handling patterns. Sample code demonstrates common scenarios like finding nearby points of interest or calculating travel times between locations. The documentation emphasizes best practices for cost management and caching to optimize performance.

BigQuery MCP integration documentation focuses on schema interpretation and secure query execution. Developers learn how to grant agents appropriate permissions without exposing sensitive data, how to structure natural language queries that translate effectively to SQL, and how to handle large result sets efficiently. Security considerations receive special attention, with guidance on data masking, row-level security integration, and audit log analysis.

Compute Engine and Kubernetes Engine MCP documentation provides infrastructure-specific patterns. For GCE, examples cover machine provisioning workflows, auto-scaling configurations, and cost optimization strategies. GKE documentation details cluster management operations, workload diagnostics, and multi-cluster coordination patterns. Both sections include failure recovery strategies and human-in-the-loop approval workflows for sensitive operations.

Google provides a practical demonstration through the “Launch My Bakery” sample application. This complete code example shows how to build a retail location scout agent combining BigQuery forecasting with Google Maps analysis. The sample includes ADK configuration files, MCP connection setups, and conversation flow definitions. Developers can run this example locally or deploy it to Google Cloud to understand real-world implementation patterns. The code demonstrates error handling, user confirmation workflows, and result presentation techniques—practical aspects often missing from theoretical documentation.

For organizations evaluating MCP capabilities, Google recommends starting with focused pilot projects that solve specific, measurable problems. A good initial candidate might be an internal knowledge assistant that answers employee questions using company documentation and HR systems. Another practical starting point could be an infrastructure monitoring agent that alerts teams about unusual resource usage patterns. These constrained use cases allow teams to learn MCP patterns with manageable risk before expanding to more complex scenarios.

Training resources complement the documentation. Google Cloud Skills Boost offers hands-on labs where developers practice MCP integrations in sandbox environments. Technical workshops provide deeper dives into agent architecture patterns and troubleshooting techniques. Community forums and office hours connect developers with Google engineers for specific implementation questions. This layered learning approach accommodates different experience levels while maintaining practical focus.

The Path Forward: Agents as Essential Business Partners

As MCP standardization matures across Google services, we stand at the threshold of a fundamental shift in how humans interact with technology. AI agents are evolving from experimental novelties into essential business partners that understand organizational context, access relevant tools, and take authorized actions to advance goals. This transformation won’t happen overnight, but Google’s managed MCP infrastructure provides the connective tissue needed to scale agent capabilities responsibly.

The implications extend beyond efficiency gains. When agents can reliably access enterprise data and systems, they begin to augment human decision-making in profound ways. Marketing teams gain assistants that analyze campaign performance across channels and suggest optimizations based on real-time data. Support organizations deploy agents that resolve common issues while escalating complex cases to human specialists with full context. Executives interact with strategic advisors that synthesize financials, market trends, and operational metrics into coherent narratives.

This vision requires moving beyond isolated agent experiments to integrated agent ecosystems. Future applications will combine multiple specialized agents working together—perhaps a data analysis agent collaborating with a visualization agent to create reports, then handing off to a communication agent that distributes insights to stakeholders. MCP’s standardized connections make these multi-agent workflows feasible by ensuring consistent interaction patterns across services.

Google’s approach balances powerful capabilities with necessary guardrails. Enterprise-ready features like granular permissions, comprehensive auditing, and threat protection ensure agents remain trustworthy partners rather than security liabilities. The progressive automation model—where agents suggest actions for human approval before execution—builds confidence through transparent, controllable interactions. This measured approach recognizes that agent adoption is a journey requiring trust at each step.

The commitment to open standards through the Agentic AI Foundation signals long-term viability. By collaborating with industry partners to evolve MCP, Google ensures the protocol addresses real-world challenges rather than theoretical concerns. This cooperative approach prevents fragmentation while accelerating innovation. Developers gain confidence that skills learned building Google MCP integrations will transfer to other compatible platforms, protecting their professional investments.

For organizations considering agent adoption, the message is clear: the infrastructure for practical, secure agent deployment now exists. Google’s managed MCP servers eliminate previous integration barriers while providing enterprise-grade reliability. Starting with focused use cases delivers tangible value while building internal expertise. As teams gain confidence, they can expand to more complex workflows that transform how work gets done.

Conclusion: Building the Agentic Future Together

The launch of official MCP support across Google services represents more than a technical announcement—it signals a fundamental shift in how AI systems interact with our digital world. By providing managed, standardized connections between advanced reasoning models and practical tools, Google removes significant barriers to creating genuinely helpful AI agents. These aren’t just chatbots with expanded capabilities; they’re proactive assistants that understand goals, access relevant resources, and take authorized actions to move work forward.

This transformation matters because it addresses the core limitation that has held back practical AI adoption. Intelligence alone isn’t enough; AI systems need reliable pathways to impact the physical and digital environments where real work happens. MCP provides that connective tissue, and Google’s enterprise-grade implementation ensures these connections are secure, observable, and manageable at scale.

As developers begin building with these new capabilities, they’ll discover that MCP’s true value lies in composability. Agents that combine BigQuery analysis with Maps context, or that coordinate GKE operations with security policy enforcement, demonstrate capabilities greater than the sum of their parts. This composability enables solutions to complex, multi-faceted problems that previously required teams of specialists to address.

The journey toward truly agentic AI remains ongoing, but Google’s MCP investment provides critical infrastructure for this evolution. By standardizing connections through open collaboration, prioritizing enterprise security requirements, and delivering managed reliability, Google creates conditions where developers can focus on agent value rather than integration complexity. The retail location scout example illustrates this potential—not as a futuristic concept but as a practical application available today.

Organizations should view this moment as both opportunity and invitation. The infrastructure for building production-ready agents now exists on Google Cloud. Teams can start small with focused use cases that demonstrate clear value while building internal expertise. As confidence grows, these initial implementations can expand into more sophisticated workflows that transform how businesses operate. The key is beginning the journey with practical applications that solve real problems.

Google’s commitment to this ecosystem extends beyond today’s announcements. As a founding member of the Agentic AI Foundation, Google will continue contributing to MCP’s evolution through open collaboration. Future service integrations will expand agent capabilities across the entire Google Cloud portfolio. This long-term perspective ensures today’s agent investments remain viable as the ecosystem matures.

The future of work won’t involve humans replaced by AI, but humans empowered by AI agents that handle routine complexity while people focus on creative problem-solving and relationship building. Google’s MCP infrastructure provides the foundation for this collaborative future—one standardized connection at a time. By giving agents reliable pathways to tools and data, we free developers to imagine what these agents might accomplish next. The tools are ready; now we build the future together.

For developers eager to begin, comprehensive documentation and sample applications provide practical starting points. The MCP overview guides new users through core concepts, while service-specific documentation offers implementation details for Maps, BigQuery, GCE, and GKE integrations. The “Launch My Bakery” sample application demonstrates real-world patterns for combining multiple MCP services into a cohesive agent experience. These resources transform theoretical possibilities into practical implementations, accelerating the journey from curiosity to production.

As this ecosystem evolves, the most valuable innovations will likely emerge from unexpected combinations—agents that connect previously isolated data sources, automate previously manual workflows, or provide insights previously hidden in complex systems. By standardizing the connections through MCP, Google creates conditions where these innovations can emerge organically from developer creativity rather than being constrained by integration complexity. This open foundation invites everyone to participate in building the agentic future, one practical solution at a time.