We present ArgTune, a modular pipeline for Arabic function calling developed for the AISA-ArabicFC shared task. ArgTune separates tool selection from argument extraction using three LoRA-fine-tuned AISA-AR-FunctionCall-Think (270M) models: a tool-selection model, a general argument model, and a specialist model for seven challenging tools, allowing each model to focus on a more specific task. Targeted augmentation, data balancing, and query-grounded post-processing further address difficult argument cases. On the official test set, ArgTune achieved 0.9964 FnAcc, 0.8506 ArgEM, and an overall score of 0.9089, ranking 8th. The results highlight the remaining difficulty of exact argument extraction despite highly accurate tool selection.