AWS published new guidance on designing MCP tools for AI agents. The post highlights where MCP tool design can go wrong and offers practical context-engineering approaches to improve reliability.
MCP has quickly become a common way to connect AI agents to external systems, but tool boundaries and schemas strongly affect agent behavior. Poorly designed tools can create ambiguity, brittle actions, or unsafe workflows.
The guidance is useful for teams moving from simple demos to production agent integrations that need predictable behavior across tools and services.