Before using the datasets to train a model, one typically needs to convert the structures into fixedlength feature vectors. This is required because most machine learning algorithms can only operate on fixed-sized inputs. For instance, we can envision that we want to predict the gravimetric gas uptake of a MOF-a property that should be the same regardless of whether we create a supercell, translate, or rotate the unit cell. That is, we need a function with which the cells of different sizes, or ordering of atoms, are mapped to the same feature vector. This example already illustrates that such a conversion of a structure into a feature vector is not unique and is always connected to certain, often hidden, assumptions. Ideally, those assumptions reflect a physical or chemical understanding of the system and act as so-called inductive biases that help the learning algorithm. But any approximation always limits the expressivity of the model, and many featurization approaches neglect-by design-certain aspects of a given system. Additionally, there are always certain design choices (such as the numbers that are used to encode chemical elements) that are not ideal for all applications. In mofdscribe we propagate those approximations (such as elemental encodings or aggregations) to the user and therefore also allow tuning those parameters to increase predictive performance.