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Evaluate AI response quality and behavior patterns during development in real time.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "AI Evaluator MCP Server" yet — see the docs or source repo.
Use this MCP tool to evaluate the following AI response, focusing on factual accuracy, instruction following, and safety, then return scores and improvement suggestions: {{AI response}}A structured evaluation with category scores, issue explanations, and optimization suggestions.
Using Petri-style behavioral assessment patterns, design a set of test cases for a customer support AI covering refusal, clarification, multi-turn consistency, and risk control, and explain the evaluation criteria for each.
An executable set of behavioral test cases with corresponding evaluation criteria.
Integrate this MCP tool into my AI development workflow to automatically run Inspect AI evaluations on each model output and summarize failures, trend changes, and issues to prioritize.
A continuous evaluation report showing failed samples, trend changes, and remediation priorities.
Evaluate AI agent outputs for CI gates, regressions, and canary promotions.
Run LLM evaluations, experiments, and custom evaluators through a standardized MCP interface.
Run prompt and RAG evaluations through MCP clients with hosted backend execution.
Validate AI-generated code with browser tests, evidence capture, and smart diagnostics.
Autonomously evaluate web apps to uncover functionality, performance, and usability issues.
Evaluate MCP agent outputs with standardized quality, safety, and cost scoring.