As long as the operations you're doing aren't too numerically unstable, you could have it use monte carlo methods for moderate numbers of inputs. Sample, say, 10,000 values across the inputs and output averages, output min, 5th percentile, median, 95th percentile, max. Right now I do this either with Python or some auxiliary column full of random values.
If you were doing it that way, you could also set distributional values for the variables. Rather than x = {500, 1000}, you could set (say) X ~ N(750, 100) and have it pull samples from that distribution. If you really wanted to get fancy you could take advantage of known results on operations on distributions to keep things exact for a lot of common calculations, then turn to numerical methods for uglier operations (at which point you're basically building a wrapper around one of the usual statistical libraries, I suppose).
(EDIT: apparently this already exists, see other comments in this thread.)