Reading the number is direct. A value near 1 is a strong yes and a value near 0 is a strong no. Near 0.5 the model gives both answers similar probability. The docs’ example asks “Is the customer asking for a human agent?” against a support message and returns {"type":"noul","noul":0.99}.
Gating a Noul means picking thresholds on the probability itself, since there is no confidence field. A screening check might act above 0.9, ignore below 0.2, and send the band in between to review. That is a different setup from Choice and Score, where the answer and the confidence in it are separate numbers you can combine.
Where the word “Noul” comes from is not documented. TypeSafe’s primitive page defines the behaviour and says nothing about the origin of the term, so any explanation you find elsewhere is a guess.
Noul suits binary screens that run on every request, such as whether a message contains a prompt injection attempt or whether a retrieved passage supports the claim it is cited for.