Multidimensional Scaling with Categorical Data

using MDS requires an understanding of the individual feature's units; maybe we are using features that cannot be compared using the Euclidean metric. For instance, a categorical variable, even when encoded as an integer (0= circle, 1= star, 2= triangle, and so on), cannot be compared using Euclidean (is circle closer to star than to triangle?).

I accept the statement above, but it raises a few question about the application of MDA:

• Given the fact that many conventional data sets contain categorical features, does it mean that MDA cannot fit to these sets?
• Maybe it would be a solution to change the distance measure type (e. g. "Euclidean") to other, but Sklearn has no other built-in option, not to mention R, where cmdscale has no option at all to specify distance type. How to change this feature in general?

An additional question: I have read that PCA is a kind of MDS (or vice versa), apart from the fact that the former focuses on variance, the latter on keeping distance. Am I right that the two "converges" somehow (in case of visualization with the two first component, for instance)?