MedToolica: Finetuning-Free Agentic Compositional Tool Learning for 3D CT Reasoning

Clinical reasoning over 3D CT scans is inherently compositional, requiring the integration of anatomical measurement, pathology assessment, spatial comparison, and clinical interpretation. We introduce MedToolica, a finetuning-free, role-based agentic framework for quantitative 3D abdominal CT reasoning that decomposes complex queries into structured sub-tasks coordinated through specialized expert tools. Empirical evaluation across quantitative reasoning benchmarks demonstrates that MedToolica is particularly effective in organ-centric measurement tasks when supported by reliable expert tools, achieving strong quantitative agreement (e.g., CCC=0.99 for organ HU estimation versus 0.46 for finetuned baselines) and notable gains on multi-step visual reasoning tasks. In contrast, lesion-oriented tasks remain constrained by upstream tool limitations, indicating that reasoning sophistication alone cannot compensate for unreliable perception. Furthermore, we observe that the capability of the core language model substantially influences orchestration quality: smaller LLM orchestrators exhibit reduced overall accuracy due to higher execution failure rates (25% vs. 79%) and increased susce