While data-intensive approaches are becoming more popular in reticular chemistry, they are still far from being routine and standard; there are currently no standard practices for digital reticular chemistry. We identified the lack of easy-to-use featurization methods and problems with model validation and comparison as the key limitations hampering the progress of the field. To address those impediments, we developed a Python package, mofdscribe, that provides utilities along each step along the path from ideation to model publication. The mofdscribe package provides increased accessibility to machine learning for reticular chemistry without compromising on rigor, especially for less experienced users. While we intend to add new features to the library and maintain it, we also hope to embrace a community effort in which bugs are fixed and new features are added by the reticular chemistry community. To facilitate this, we designed our library so that it is easy for researchers to implement new strategies, such as featurizers, in our library so that their work can easily be reused by other digital chemists. We hope that, together with the open availability of machine-actionable data, our developments will systematize and accelerate machine learning for reticular chemistry.