Last month a national magazine wrote about people using Claude as a sommelier. The writer had inherited a cupboard of old bottles from a great-aunt, photographed the labels, and asked Claude what she had. It told her the 1982 Meursault was oxidised and past saving, and that the 1972 Bordeaux came from one of the worst vintages of the decade. Both answers were correct. The piece was still, fairly, a bit sniffy about the whole exercise.
My app (https://cellarion.app) got a passing mention in it, in scare quotes, which is how I ended up thinking about why that experiment was doomed from the start.
Claude did nothing wrong there. It gave good general answers. The problem is that "is this specific bottle worth opening" is not a general question, and no amount of world knowledge gets you to it. That gap is the entire reason MCP exists, and wine turns out to be an unusually clear way to see it.
The gap
A language model knows an enormous amount about wine in general. It knows that white Burgundy from the early eighties is a lottery, that 1972 was a washout in Bordeaux, that Nebbiolo needs time. All of that is in the training data, freely available, and genuinely useful.
