Commonly used are the number of subjects in a trial, or some function of the standard error. If the plot is symmetric, like an inverted V, this is interpreted as demonstrated that there is probably no publication bias. If the plot is asymmetric, the interpretation is that publication bias is likely. There is no empirical evidence to support this notion. Yet the interpretation of asymmetrical plots is often that there must be unpublished negative trials that would serve to negate the positive findings of a meta-analysis if only they could be found.
Philosophers have a word for it.
Falsification means that you can't prove that its stupid though its stupidity stares you in the face. They then produced different funnel plots using size and using standard error. There was also a suggestion that asymmetry in at least one of the plots was present in those meta-analyses where it was least likely to be expected. If you have a number of trials with the same sort of patients, with the same disease severity, the same intervention, and the same outcome over the same time, then what you have is a homogeneous data set. That's how heterogeneity tests are set up.