Building AI Agents on Google Cloud with MCP, ADK, and A2A

Core AI Agent Development Concepts

• 💡 MCP (Model Context Protocol) acts as a reverse proxy or API server, standardizing how AI models connect to necessary resources, prompts, and tools.
• 🧩 ADK (Agent Development Kit) provides a framework for building and deploying agents, aiming to make complex agent creation feel like standard software development.
• 🤝 A2A (Agent2Agent Protocol) offers a common language for agents to communicate and collaborate, regardless of their origin or framework.

Setting Up Your Google Cloud Environment

• ☁️ Google Cloud Project setup is required with billing enabled, and all work will be conducted within CloudShell.
• 🛠️ Essential APIs for Cloud Run, Cloud Build, Artifact Registry, and Vertex AI must be enabled.
• 🐍 Ensure Python 3.10 or newer is available, and clone the project's repository from GitHub.

Building and Deploying the MCP Server

• 💰 A currency exchange rate tool is built using the FastMCP Python package.
• 🚀 The MCP server is initially run locally and then deployed to Cloud Run for scalability and security, using a no-allow-unauthenticated flag.
• 🔒 A Cloud Run proxy is used to create a secure, authenticated tunnel for testing the deployed server.

Creating and Exposing the AI Agent

• 🎯 The ADK is used to build the AI agent, defined by a system instruction and an MCP toolset that connects to the deployed server.
• 🌐 A simple web interface for the ADK agent is started, allowing interaction via a web preview.
• 🗣️ The agent is exposed via A2A, which standardizes agent collaboration using an agent card advertising its skills.

Testing and Cleanup

• 🧪 An A2A server is launched, and its functionality is tested using the A2A Python SDK's client class.
• ✅ Successful A2A communication confirms that other programs and agents can interact with the currency agent.
• 🗑️ Resource cleanup is crucial, with the recommendation to delete the Google Cloud project used for the tutorial to avoid future charges.