Evaluate Model Context Protocol servers and AI agents with real trace analysis. Discover why 80% of failures happen at the tool selection stage.
LLM-as-a-judge evaluators only look at the final output, missing critical failures in the execution trace.
Task: “Send email from Gmail”
Result: “Email sent successfully”
Score: ✓ CorrectTask: “Send email from Gmail”
→ Selected: Outlook (wrong tool)
→ Retried 3 times
→ User frustrated
Real Score: FailedAnalyze the full execution trace, not just the final output. Catch tool selection failures, disambiguation issues, and retry loops.
Identify which tools are failing and why. Get actionable insights on authentication issues, missing parameters, and API errors.
Monitor your agent performance with daily reports. Track success rates, latency, and tool usage patterns across your team.
“We discovered our agent was picking the wrong email tool 80% of the time. The trace analysis showed us exactly why - our tool descriptions were ambiguous. Fixed it in a day and our success rate jumped from 70% to 95%.”
Manoj, CEO at AgentR