For more efficient modelling, coarse-grained (CG) or minimalistic models simplify and approximate molecular representations to low-resolution models. By nature, their low resolution provides access to low-cost simulations, allowing larger systems to be modelled for longer times, or in this case, allowing for vast numbers of systems to be modelled. In particular, minimalistic models (or "toy models") allow us to zoom out to very low resolutions and systematically generate large amounts of qualitative data to learn from, eventually translating that into atomistic models or experimental designs. With complete control over the model parameters, toy models allow for extrapolation beyond known chemical space. For example, Martin et al. show in a series of papers how minimal models that capture the structural features of metal-organic frameworks can aid in interpreting the limits of materials optimisation. Similarly, Wolpert et al. used CG models of cages, treating them as hardpolyhedra, to map the phase space of their packing behaviour as a function of simple interactions. Evans et al. use a minimal model to explore placement and configuration effects on molecular motors in metal-organic frameworks. Using high-resolution models, Pesce et al. studied crowding-reactivity relationships in host-guest binding by artificially modifying model parameters. Here, we apply a bottom-up computational approach to build 1000s of minimal cage models using stk and OpenMM. This work does not represent a rigorous coarsegraining based on some experimental or higher resolution data but focuses on the role of a few building block parameters on the geometric stability and eventual cage structure of different topologies. We outline how this work could feed into future computational and experimental cage screening and decisionmaking. In particular, we use this model to efficiently explore parameter space and study the effect of distinct features, such as preorganisation, which are difficult to isolate in atomistic models. We design the model and associated software toward automated rational design and we have made the outcomes of these predictions freely available online in an easily accessible manner for use by experimental researchers.