Moving Beyond LLM Hallucinations in Structural Engineering via Deterministic MCP Tools

Description and use

<p>Large Language Models are exceptional at synthesis but notoriously unreliable at geometry and regulatory compliance. In a domain like structural design—specifically staircases—an LLM's tendency to hallucinate decimal precision isn't just a UX nuisance; it is a liability. If an agent suggests a riser height that violates International Residential Code (IRC) standards, the error moves from digital text to physical risk.</p> <p>When we began developing specialized Model Context Protocol (MCP) servers, the objective wasn't to give an AI 'knowledge' of building codes, but to give it 'agency' ove

Problem

<p>Large Language Models are exceptional at synthesis but notoriously unreliable at geometry and regulatory compliance. In a domain like structural design—specifically staircases—an LLM's tendency to hallucinate decimal precision isn't just a UX nuisance; it is a liability. If an agent suggests a riser height that violates International Residential Code (IRC) standards, the error moves from digital text to physical risk.</p> <p>When we began developing specialized Model Context Protocol (MCP) servers, the objective wasn't to give an AI 'knowledge' of building codes, but to give it 'agency' ove

Outcome

<p>Large Language Models are exceptional at synthesis but notoriously unreliable at geometry and regulatory compliance. In a domain like structural design—specifically staircases—an LLM's tendency to hallucinate decimal precision isn't just a UX nuisance; it is a liability. If an agent suggests a riser height that violates International Residential Code (IRC) standards, the error moves from digital text to physical risk.</p> <p>When we began developing specialized Model Context Protocol (MCP) servers, the objective wasn't to give an AI 'knowledge' of building codes, but to give it 'agency' ove

Evidence

Moving Beyond LLM Hallucinations in Structural Engineering via Deterministic MCP Tools

Source