I'm a complete noob and I'm not sure if my case translates to the case you're describing, but I'll have an attempt and accept criticism.
From my experience, the problem with F1-score is that it doesn't consider true-negatives. This means that in the case of heavily inbalanced datasets, the false-positives (when considering the minority class) will dominate, since we do not consider how big the proportion of false-positives is of all the negatives.
Consider a classifier predicting like this:
Of all the positives, things are looking good, but the F1-score will punish this model by the relation between false-positives and true-positives.
But what if those 100 false-positives come from a set of 1000 negatives, which means the classifier has a 10% chance of predicting a false-positive - compared to if that is from a million negatives, in which case the chance is 0.0001%. The latter is more impressive. This is not a problem when considering models trained on the same datasets, but the F1-score requires a bit more context to understand what it translates to in terms of performance, and especially when comparing across datasets.
I believe the Matthews_correlation_coefficient solves the problem by considering all 4 elements of the confusion-matrix.
If we plot the MCC-score as true-negatives approaches infinity, we see that MCC increases, since the proportion of false-positives gets smaller and smaller.
I still need to test the metric on more cases, and I don't know if this relation is only apparent with small numbers like in my example.