One way to define a test suite that would be very easy for readers to understand could be to rig a test suite of "table-driven tests" for some toy problem.
I'm not sure what a good toy problem would be that would be small enough so it's relatively easy for readers to understand, but large enough to show off MDL being applicable to a more general kind of programming problem and not merely MDL being applied to a statistical modelling exercise (one person's decision tree fit via supervised learning is another person's handwritten function containing a morass of hand written if-else chains -- but we already expect MDL to give reasonable results when applied to decision trees, it's less obvious it gives good results when applied to more general programming problems)
c.f. https://dave.cheney.net/2019/05/07/prefer-table-driven-tests
In my mind this is also related to concepts like scientific notation. Minimum description is not just about minimizing the number of operations, the number of branches, the degree of freedoms of your program. Taking into account variable width also makes a difference. Using a timestamp YYMMDDHHMMSS when you only need YYMMDD creates unintended edge cases the same way using too many digits in scientific notation creates noise and both can result in poor calibration with the uncertainty of the model.