Building a Multi-Server MCP Using AWS Bedrock Agent

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 Agent 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 an agent interacts with a remote MCP HTTP bridge, how multiple independent MCP servers collaborate, and how managed services and serverless components are composed in real deployments. The goal is to simulate realistic architecture and communication flows where components run across AWS services (Lambda, EC2, DynamoDB) and the agent invokes remote operations.

The comprehensive operational flow of the lab begins with the user interacting with the Bedrock Agent, which then invokes a Lambda handler to communicate with the EC2 instance hosting the MCP servers for “Weather” and “Task” operations. These operations utilize DynamoDB for persistent data storage, demonstrating real-world AI integration.

After completing this Cloud Lab, you will have enough knowledge to configure and deploy an Amazon Bedrock Agent with action groups. You will have gained practical experience in integrating Lambda with external EC2-hosted services (MCP) and managing persistent data in DynamoDB, which is essential for building secure, custom, and enterprise AI solutions.