Previous studies in machine learning (ML) for solid state chemistry have primarily focused on applying state-of-the-art algorithms and improving model accuracy. However, there is a growing interest in using ML as a primary tool to gain deeper insights into the underlying phenomena, which means improving explainability of the models. These studies emphasize explainability based the on features generated with open-source packages or software managed by individual labs. The features can be broadly categorized into compositionbased and structure-based types, although other cases, such as microstructure or sample imaging processing, might have a narrow specialization and rely on highly specialized databases. In this paper, we explore open-source packages available for generating chemistry-based features, introduce two Python open-source tools: Composition Analyzer/Featurizer (CAF) and Structure Analyzer/Featurizer (SAF). We demonstrate the performance of the features generated with CAF and SAF in classifying the equiatomic AB intermetallic crystal structures. Composition-based features can be generated from a chemical formula by parsing the formula into the constituent elements and their stoichiometric ratios. Due to its simplicity, there are open-source software packages capable of generating features in a high-throughput way. The composition-based feature vector (CBFV) package from the Sparks group is an example that utilizes multiple databases for a given chemical formula. Matminer is another open-source toolkit which contains 44 featurization classes capable of generating thousands of descriptors. It also provides additional functionalities, including visualization, data retrieval from large databases such as the Materials Project, Citrine Informatics, Materials Data Facility and the Materials Platform for Data Science. addresses challenging topics, such as prediction of conditions for organic reactions. However, RDKit primarily focuses on molecular structures, with unknown applicability in extended crystal structure prediction. Similarly, Mordred is a widely used featurizer by Takagi which produces close to 2,000 features and is used in experimentally-validated medicalrelated studies, such as drug repurposing screening to identify clinical drugs targeting SARS-CoV-2 main protease and an open drug discovery competition for novel antimalarials. Despite its applications in other fundamental chemistry studies, such as predicting the reactivity power of hypervalent iodine compounds, like RKDit, Mordred is not focused on solid state materials. Additionally, MOFormer by Cao is also software for metal-organic frameworks (MOFs), not intended to be used as a general featurizer for inorganic solid-state materials.