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Scored across 9 tools
Each tool maps to a distinct pipeline stage (training, sampling, per-model prediction, metrics, selection, explanation, export). The only mild overlap is predict_both versus the two single-model predict tools, but the descriptions make the convenience-versus-specific distinction clear.
Most names follow a verb_noun pattern (train_models, get_sample_input, predict_knn, select_best_model, export_parquet_audit). Two deviate: confusion_matrix_metrics is a bare noun phrase and shap_explain_selected is an awkward ordering, but the set remains readable and predictable.
Nine tools neatly cover one end-to-end classification workflow with no padding. Each tool earns its place: train, sample, predict, evaluate, select, explain, export.
The full lifecycle is covered: training, sample input, prediction for both models, agreement check, metrics, model selection, SHAP explanation, and audit export. Minor gaps exist (no explicit way to inspect/list saved models or retrain a single model), but agents can work around them.