Building Connected Agents with MCP and A2A

To build a production-ready agentic system, where intelligent agents can freely collaborate and act, we need standards and shared protocols for how agents talk to tools and how they talk to each other.

In the Agent Production Patterns module in the Production-Ready AI with Google Cloud Learning Path, we focus on interoperability, exploring the standard patterns for connecting agents to data, tools and each other. Here are three hands-on labs to help you build these skills.

The Foundations of ADK, MCP, A2A

This lab serves as your "Hello World" for the modern agent stack. You will build a simple, specialized agent that demonstrates how Agent Development Kit (ADK), Model Context Protocol (MCP), and Agent to Agent Protocol (A2A) work together.

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Lab: Getting Started with MCP, ADK and A2A

Objective: Build a specialized currency agent that handles exchange rates, demonstrating the core building blocks ADK, MCP, and A2A communication.

Connecting to Data with MCP

Once you understand the basics, the next step is giving your agent access to knowledge. Whether you are analyzing massive datasets or searching operational records, the MCP Toolbox provides a standard way to connect your agent to your databases.

Expose a BigQuery database to an MCP Client

This lab shows you how to expose BigQuery tables to an MCP client.

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Lab: MCP Toolbox for Databases: Making BigQuery datasets available to MCP clients

Objective: Configure the MCP Toolbox to expose a public BigQuery dataset to an AI agent, enabling it to query and analyze datasets using natural language.

Expose a CloudSQL database to an MCP Client

If you need your agent to search for specific records—like flight schedules or hotel inventory—this lab demonstrates how to connect to a CloudSQL relational database.

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Lab: Build a Travel Agent using MCP Toolbox for Databases and Agent Development Kit (ADK)

Objective: Build a full-stack agent that interacts with a Cloud SQL database to search for flights and hotels, demonstrating how to securely expose relational data to an AI agent.

From Prototype to Production

By moving away from custom integrations and adopting standards like MCP and A2A, you can build agents that are easier to maintain and scale. These labs provide the practical patterns you need to connect your agents to your data, your tools, and each other.

These labs are part of the Agent Production Patterns module in our official Production-Ready AI with Google Cloud Learning Path. Explore the full curriculum for more content that will help you bridge the gap from a promising prototype to a production-grade AI application.

Share your progress using the hashtag #ProductionReadyAI. Happy learning!