In general the added value of MC simulations is to be able to vary all of the uncertain input parameters at the same time. Otherwise, you might as well use other forms of one-way sensitivity analysis. So you probably want run the simulation varying all parameters simultaneously for 1000 or actually 10000 times or greater (more is generally better but with diminishing marginal returns for each additional simulation, and some practical considerations on computational time).
A word of caution: You say you have various parameters, and each of those parameters can take 100 values. Do each of those 100 values have the same 1% chance of occurring, i.e. a uniform distribution? If not, it’s much more appropriate to apply some parametric assumptions, eg normal or beta or whatever of various distributions most accurately describes the situation you are modelling. If you truly have uniform distributions across all the input parameters, your final MC simulation is most likely going to give you a linear pattern from lowest to highest possible outcome, which doesn’t really add much value beyond a deterministic analysis that could be done with arithmetic.