I would argue the opposite... and that as far as tests for Normal distributions are concerned, Jarque-Bera is the most transparent and explicit since it captures a combination of Skewness and Kurtosis which are the two dimensions that capture divergence from a Normal distribution. And, to my knowledge it does that better than any other tests for Normal distribution.
One may argue that Jarque-Bera is too sensitive to sample size. The larger the sample the more a trivial divergence from the Normal Distribution will become statistically significant. However, this is true of all such Normal Distribution tests. Actually, this is true of the entire body of hypothesis testing that relies on p-value instead of Effect Size.