High-resolution mass spectrometry (HRMS) and high-throughput H NMR spectroscopy were selected as data characterization techniques for the automated synthetic screen and used in conjunction with one another. Overall, HRMS analysis indicated reactivity of the precursors and identified whether a MNLN (where N = number of incorporated units) or M2X3 (where X = L or I) species of interest had formed, while the conversion was analyzed by 1 H NMR spectroscopy. Where unreacted aldehyde precursor was observed this indicated that the reaction had not gone to completion. This two-step approach filters the results from the screen, with only full characterization conducted on any 'hits', although assignment of the 1 H NMR spectra was attempted for all spectra collected to deduce the reaction outcome and identify the major assembly. To streamline the analysis of the characterization data, the open-source code from our cage database tool cagey was adapted to create both moc_ms_analyser.py and moc_nmr_analyser.py (workflow-mocs), enabling automated analysis. First, a Python script was developed to calculate likely possible structures that may form between the six-coordinate metal ion, tri-topic ligand and the di-topic intermediate (where the triamine has undergone two imine condensation reactions with a given aldehyde but a free amine remains), and with a varying number of counterions. For each reaction, the metal, triamine, aldehyde, and counterion SMILES were loaded as a Python dictionary and the empirical formulae were calculated for the metal ion, tritopic ligand, ditopic intermediate, and counterion. The combinations of these components into possible assemblies, with their corresponding empirical formulae, charges, and isotope splitting patterns, were subsequently calculated. The experimental HRMS data was then searched for the m/z isotope splitting of each of the calculated possible solutions, and if found, written to a data frame containing: the formula of the structure, found m/z, found intensity, predicted m/z, predicted intensity, and predicted charge. The data frame was then ordered by formula and the m/z splitting between the peaks was calculated to assign the charge. The results were filtered down to formulae found where the observed charge matched the predicted charge, yielding a machine-readable data frame of the HRMS results.