MCP + AWS AgentCore: Give Your AI Agent Real Tools in 60 Minutes
If you've been building with AI agents, you've probably hit the same wall I did: your agent needs to do things — query databases, call APIs, check systems — but wiring up each tool is a bespoke int...
Source: DEV Community
If you've been building with AI agents, you've probably hit the same wall I did: your agent needs to do things — query databases, call APIs, check systems — but wiring up each tool is a bespoke integration every time. The Model Context Protocol (MCP) solves this by giving agents a standard way to discover and invoke tools. Think of it as USB-C for AI tooling. The problem? Most MCP tutorials stop at "run it locally with stdio." That's fine for solo dev work, but it falls apart the moment you need: Multiple clients connecting to the same server Auth, session isolation, and scaling A deployment that doesn't die when your laptop sleeps AWS Bedrock AgentCore Runtime changes the equation. You write an MCP server, hand it over, and AgentCore handles containerization, scaling, IAM auth, and session isolation — each user session runs in a dedicated microVM. No ECS clusters to configure. No load balancers to tune. In this post, we'll build a practical MCP server from scratch, deploy it to AgentC
