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.

We present SanadAI, a modular system for the IslamicEval 2026 shared task on fine-grained hallucination detection and correction in Arabic Islamic content. We participate in Subtasks 1 and 2, which address citation span detection and hallucination detection, respectively. For Subtask 1, we fine-tune AraModernBERT as a BIO sequence tagger to identify Quranic verses ayah, Hadith text matn, transmission chains isnad, and claimed sources. Controlled Quranic and Hadith augmentation, together with Shamela-based hard-negative selection, substantially improves citation span detection over training on competition data alone. For Subtask 2, we use source-specific verification pipelines: Quranic evidence is verified through Arabic normalization and approximate matching, while Hadith evidence is verified using BGE-M3 dense retrieval, FAISS candidate search, lexical reranking, and semantic and entity-level evidence. SanadAI achieves 97.35% Macro-F1 on Subtask 1 and 86.66% official macro-accuracy on Subtask 2 on the hidden test set. Quranic verification is strongest at 96.89%, whereas isnad verification remains the most challenging at 77.42%, reflecting the difficulty of verifying transmission chains when the system relies on narrator compatibility with a retrieved Hadith rather than explicitly modeling narrator order and chain continuity.