MCP Tool Evaluation and Testing in Agent Evals
Dynamic tool discovery breaks traditional pass-fail testing designed for fixed tool menus.
Staff Writer
Dmitri holds a background in distributed systems research and spent several years contributing to open-source observability tooling before joining The Harness. He covers the nuts-and-bolts engineering work that goes into building, testing, and shipping reliable agent infrastructure.
7 stories
Dynamic tool discovery breaks traditional pass-fail testing designed for fixed tool menus.
Catch AI agent failures hidden in production traces before they hide in averages.
Human reviewers must anchor agent evaluators to real production behavior.
Deep visibility into state changes and loop cycles reveals why LangGraph agents fail.
Production traces reveal real failure patterns that synthetic tests miss entirely.
A taxonomy reveals how to trace agent failures back to ambiguous prompts systematically.
Freeze production failures in test by replaying incidents with real tool outputs.