We showcase the importance of library cleaning on the publicly available GNPS library, see Figure and 3 (GNPS Library, 2023). All spectra undergo some form of metadata harmonization. In addition, 53892 spectra did not have a computer readable annotation and 29242 spectra did have an annotation or parent mass that did not internally match, which highlights the importance of library cleaning, before using it for training machine learning models. This pipeline repairs errors in the metadata for spectra that are already uploaded to public libraries. A more desirable approach to improve mass spectral library quality in the future would be to curate spectra during the uploading of new library spectra, instead of repairing mistakes after the fact. One of the main challenges here is that this is mostly relying on voluntary work; hence, such a process should not be too time-consuming. Therefore, there is a need for easy and fast methods for automatic curation while uploading to public mass spectral libraries, ultimately improving their metadata quality and coverage. We expect that the here described developments can contribute to such a validator tool, that will also automatically fill metadata fields that can be propagated from other fields, and flag internal metadata inconsistencies.