Engineering Compliance Prover MCP Connector for Claude
A+Forces AI to validate structural designs against US codes (ASCE, ACI, NEC). Demands real capacity-demand ratios, traced load paths, specific material tolerances, and FMEA instead of vague appeals to 'industry standards'.
AI agents propose engineering designs that look plausible but fail fundamentally on safety factors, load paths, and code compliance. They rely on vague appeals to 'industry standards' rather than calculating specific capacity-demand ratios or tracing loads through a system. In engineering, 'looks sturdy' is not a metric.
The Problem It Solves
AI-generated engineering reasoning fails for five specific reasons:
- Code blindness — Vague references to "best practices" without citing specific normative codes (e.g., ASCE 7-22).
- Ignored failure modes — Prescribing a solution without analyzing HOW it could fail (yielding, buckling, fatigue) and identifying the controlling condition.
- Ungrounded safety factors — Failing to calculate explicit capacity-demand ratios or factors of safety.
- Broken load paths — Missing load assumptions (dead, live, seismic) or failure to trace forces through the structure.
- Omitted tolerances — Ignoring material grades, specifications, and environmental constraints. 'Steel' is not a spec.
How It Works
Engineering Compliance Prover uses 5 Decision Pivots grounded in US engineering practice:
- codeComplianceValidated — Is the design explicitly validated against a stated, specific normative code?
- failureModesAnalyzed — Are the critical failure modes explicitly analyzed, identifying which one controls?
- safetyFactorsChecked — Are capacity-demand ratios or factors of safety explicitly calculated and confirmed?
- loadPathsTraced — Are all load assumptions explicitly stated and their path traced?
- tolerancesSpecified — Are specific material grades, tolerances, and environmental conditions addressed?
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