Choosing features is difficult with few labeled molecules, even for a tabular foundation model that learns in context. We study this problem with TabPFN for low-data molecular property prediction, separating input width, feature meaning, and predictive usefulness while keeping contexts, query molecules, and predictor settings matched. On ADME/Tox assays from Therapeutics Data Commons, endpoint-matched assay predictions improve a compact RDKit descriptor baseline, and a provenance audit shows that most audited query molecules already occur in the predictors' training sources. Across 77 FS-Mol development tasks and 157 held-out test assays drawn from ChEMBL, broad RDKit descriptor and fingerprint families provide a strong default, rank-averaging TabPFN with support-fitted fingerprint forests improves upon both models, and learned weighting of family predictions adds further gains at a larger prediction budget. A utility-trained selector builds a traceable 16-column input from 238 sourceassay predictors, improving upon the baseline while trailing wider representations and equalwidth structural SVD coordinates. Named outputs identify what each feature predicts and where it comes from. Their usefulness for a new assay is assessed alongside the other input features, and paired removal and replacement tests measure what each selected output contributes. Together, these studies offer a protocol for evaluating feature selection, representation choice, and data provenance with a fixed tabular foundation model for few-shot molecular property prediction.

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