but here we're still filtering by expressions..am I missing something?

Two reasons:

1. It will throw exception at CheckAnalysis if parameter is a DataType. So we do not need handle it here:

scala> spark.sql("select cast(int)")
org.apache.spark.sql.AnalysisException: cannot resolve '`int`' given input columns: []; line 1 pos 12;
'Project [unresolvedalias('cast('int), None)]
+- OneRowRelation

  at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$$nestedInanonfun$checkAnalysis$1$2.applyOrElse(CheckAnalysis.scala:113)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$$nestedInanonfun$checkAnalysis$1$2.applyOrElse(CheckAnalysis.scala:110)
  at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUp$2(TreeNode.scala:306)

2. It's hard to know a parameter is DataType or Expression here.

So, I think the problem here is not that we are not considering the DataType args, but the problem IMHO is that we are not handling the case validParametersCount == 0. I think in that case we should throw something like: Invalid arguments for function $name.. Indeed, in the cast case, I'd argue that it is not so clear to me whether the arguments are 1 or 2, since it is not cast(1, string) but cast(1 as string)... So I don't think that it is correct to state that the number of arguments for cast is 2.

nit: in case of 0, I'd prefer a more generic Invalid arguments for function $name, instead of Invalid number of arguments for function $name. Because as I mentioned earlier, I think it is questionable which is the number of arguments in this case...

Done