  In this contribution we investigate how to describe the results and usage of
evolutionary synthesis models. In particular, we look for an explicit and
quantitative description of the parameter space of synthesis models and the
evaluation of their associated uncertainties and dispersion. First, we need to
understand what synthesis models actually compute: we show that a synthetic
stellar population with fixed physical parameters (age, metallicity, star
formation history, initial mass function and size of the system) can only be
described in terms of probability distributions (i.e. there is an intrinsic
dispersion in any model). Second, we need to identify and characterize the
coverage in the parameter space of the models (i.e. the combinations of input
parameters that yield meaningful models) and the different sources of
systematic errors. Third, we need a way to describe quantitatively the
intrinsic dispersion, the systematic error and the parameter space coverage of
the models.
  Up to now, the parameter space coverage and uncertainties have been described
qualitatively in the models' reference papers, with potential
misinterpretations by models' users. We show how Virtual Observatory
developments enable a correct use of synthesis models to obtain accurate (and
not simply precise) results.
