Over the last few decades, numerous computational models of human brain development have been proposed to improve our understanding of cortical folding from a mechanical perspective. Beyond purely mechanical descriptions, several approaches couple tissue growth with underlying cellular mechanisms to capture bidirectional interactions between macroscopic morphological changes and microscopic processes such as cell division, migration, and the formation of neuronal connectivity [1]. These models have achieved substantial advances in geometric realism, the range of cellular events represented, and computational methodology. Despite this progress, model validation remains a major challenge. Reliable validation is hindered by (i) the limited availability of fetal morphological data suitable for quantitative comparison and (ii) the absence of a widely accepted validation framework. As a result, studies often rely on heterogeneous datasets and ad hoc evaluation methods, making cross-study comparisons difficult. To address this gap, we developed a standardized tool for quantifying morphological features in computational models of the developing brain. The tool provides a user-friendly graphical interface and supports multiple validation metrics at the macroscopic scale for both 2D and 3D outputs, including the gyrification index, sulcal depth, and additional measures. Furthermore, we integrated real medical imaging data from fetal brains spanning gestational weeks 24 to 38 to enable direct comparison between model predictions and empirical development. This framework is expected to improve the reliability of model evaluation, enable more consistent comparisons across studies, and support the development of more accurate models that better link cortical folding dynamics with underlying biological processes.