This paper evaluates the performance of Depth Anything Model v2 (DAMv2), a state-of-the-art foundation model for monocular depth estimation, in endoscopic imaging contexts. We assess its zero-shot capabilities on public endoscopic datasets, comparing it with specialized models such as Endo-Depth and Endo-SfMLearner. Our comprehensive analysis incorporates quantitative metrics, qualitative assessments, and 3D reconstruction applications. The results demonstrate the potential of foundation models to generalize across diverse anatomical structures while highlighting the complementary strengths of domain-specific approaches. This investigation reveals the capacity of DAMv2 to enhance 3D reconstruction in minimally invasive surgeries, contributing to the advancement of robust and versatile solutions in endoscopic depth estimation. The findings have significant implications for improving computer-assisted interventions and patient outcomes in clinical settings.