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Scored across 9 tools
Each tool targets a distinct pipeline stage (train, predict, evaluate, select, explain, export), and the two single-model predict tools are clearly differentiated by model name. The only overlap is predict_both, which duplicates the two single-model predict tools as a convenience, but its description makes the boundary clear.
Names are consistently snake_case and mostly verb-first (predict_*, train_models, get_sample_input, select_best_model, export_parquet_audit). Minor deviations exist where actions start with a noun (confusion_matrix_metrics, shap_explain_selected), but they remain readable and predictable.
Nine tools map cleanly onto the ML workflow (train, predict, evaluate, select, explain, export) with no filler. The count is well-scoped for a demo pipeline and each tool earns its place.
The surface covers the full lifecycle: training, single and combined predictions, metrics, model selection, SHAP explainability, sample input, and audit export. Minor gaps include no tool to read/inspect the audit log contents directly and no way to pass arbitrary custom patient input beyond the sampled test row.