Openclaw Output Vetter Mcp

This runs three inline checks to catch when AI agents hallucinate facts, silently swallow exceptions with mock data, or claim they finished tasks without proof. You get verify_response_grounding to score claims against input context using stem-Jaccard overlap, find_swallowed_exceptions to walk AST for try/catch blocks that return fake success responses, and review_transcript to flag unverified completion claims and cross-turn contradictions. It's pure Python, no API key, sub-second. Designed to be called during the conversation in Claude Code or Cursor, not as a separate eval pipeline. The use case is catching the quiet failure modes that look fine in standard dashboards but break production silently.