Raw-price thresholds can turn alerting into a permanent modeling project and supply chain intelligence source. A team must decide whether a 5% move outranks a 10% move, choose a comparison window, and set how volatility affects the rule. Then it has to tune, backtest, maintain, and defend those choices across materials that have little in […]
Category: Metals Data, AI and MCP
Why Can’t an AI System Answer a Metals Cost Question From The Price Alone?
An AI metal price number doesn’t carry meaning on its own in procurement risk management. Before an AI model can calculate, compare, summarize, or explain, it needs the market identity behind that number. Take a simple question: “Did our aluminum cost rise in 2024?” An AI system that retrieves only “the aluminum price” can answer […]
Utilizing MCP Servers for Accurate Copper Price Projections
Note to reader/how to read this article: Bracketed [CONFIRM] items require verification from the person configuring the server before publication. An AI agent should not have to guess what “copper prices” means. A properly designed copper MCP server resolves four things in a single structured request, each returned as a separate typed field rather than blended […]
An MCP-Fed Metal Exposure Dashboard Cannot Run on Metal Market Data Alone
A live metal market data cost dashboard built entirely from market data calls will misstate exposure. MetalMiner MCP (Model Context Protocol) data supplies market price, premium, surcharge, technical, and forecast inputs that eliminate manual price drops from the workflow. It cannot supply the bill of materials, demand plan, supplier contracts, purchase orders, scrap factors, or […]
How to Log and Reconstruct an AI Metal Price Answer
Metal market prices in AI prompts are not answered by an AI agent; they are an auditable evidence package, not a paragraph of generated text. A person may later ask why an answer cited a particular number. That number could be a hot-rolled coil price, a COMEX copper basis, a 304 stainless specification, a Midwest […]