I would rather ask the question how many distinct celltypes you expect rather than clusters. Clusters are (beyond the actual celltypes) a consequence of preprocessing, chpice of input genes, normalization and parameters for the clustering as well as the precise algorithm and weighting functions and the granularity at which you look at a dataset. Having an idea which celltypes you expect I would then check if the clusters you have cover these, and whether the current clustering landscape is sufficient to address your scientific question. Running cluster diagnostics is imo an addon to guide your decision if you trust the results, but I would not regard is as "necessary" and I would put biological interpretation higher on the "necessity scale" than any of these stability tools. Clustering can also produce stable nonsense, so good biological knowledge of the experimental/model setup is key to decide what is "real" in terms of "biologically meaningful" rather than just technically stable. If there are clusters you cannot explain be sure to check for duplicate clusters or clusters of trash cells, I like scDblFinder from Bioconductor for doublet detection.