Reliable function calling is challenging for Arabic due to dialectal variation and limited task-specific resources. We present Makeen, a data-centric approach for the AISA-ArabicFC shared-task, and extend it with span extraction and confidence-gated routing. The best post-submission variant achieves 83.20% argument exact match and 91.11% Track B on development data under held-out (cross-validated) routing, while the submitted model achieves 82.65% argument exact match and 91.06% Track B on the blind test set, ranking 9th in Task B. We also release two Arabic tool-use datasets and show that external-tool augmentation improves unseen-tool argument exact match from 44.49% to 66.53%. These results highlight the importance of data quality, argument grounding, and tool-level generalization for Arabic function calling.