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Evaluate AI agent outputs for CI gates, regressions, and canary promotions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-eval-mcp" yet — see the docs or source repo.
Use agent-eval-mcp to evaluate the AI agent responses from this commit against the main branch baseline. If accuracy, format compliance, or task completion falls below thresholds, return a failure with detailed metrics.
An evaluation report with scores, baseline deltas, CI gate pass/fail status, and failure reasons.
Use agent-eval-mcp to compare outputs from agent versions v1 and v2 on the same test set. Measure success rate, hallucination rate, and response consistency, then summarize which version is better for release.
A regression comparison report showing metric differences, strengths and weaknesses, and a release recommendation.
Use agent-eval-mcp to evaluate the current canary agent outputs, compare sampled production results with the historically stable version, and determine whether quality thresholds for traffic expansion are met.
A canary evaluation summary with key quality metrics, risk notes, and a recommendation on traffic expansion.
Evaluate MCP agent outputs with standardized quality, safety, and cost scoring.
Run prompt and RAG evaluations through MCP clients with hosted backend execution.
Evaluate AI response quality and behavior patterns during development in real time.
Autonomously evaluate web apps to uncover functionality, performance, and usability issues.
Test API compatibility for AI agents with scores, grades, and recommendations.
Run deterministic agent orchestration with task decomposition, subagents, and review feedback.