Model Context Protocol (MCP) is a vital communication standard for AI agents. It allows an agent to securely exchange context and data with external, custom application servers using defined functions. Mastering MCP is crucial for integrating intelligent assistants with existing enterprise systems and services.
In this Cloud Lab, you will learn to build an Amazon Bedrock AgentCore Harness that acts as the AI-powered assistant, responsible for checking weather conditions and managing training-related tasks. The lab simulates a distributed enterprise architecture, differing significantly from a single-process Python tutorial. It demonstrates how the Harness connects directly to remote MCP servers as tools, how multiple independent MCP servers collaborate, and how managed services are composed in real deployments. The goal is to simulate realistic architecture and communication flows where components run across AWS services (EC2, DynamoDB) and the Harness invokes remote MCP tools directly.
The comprehensive operational flow of the lab begins with the user interacting with the Bedrock AgentCore Harness, which connects directly to the EC2 instance hosting the MCP servers for “Weather” and “Task” operations via a Remote MCP server tool. These operations utilize DynamoDB for persistent data storage, demonstrating real-world AI integration.
