Context Web-based molecular docking servers are widely used in structure-based modeling due to their accessibility, yet their outputs are commonly evaluated using single-run pose accuracy metrics that implicitly assume deterministic behavior. Such assumptions can obscure variability arising from stochastic sampling and ranking procedures, particularly in black-box usage scenarios where replicate validation is uncommon. In this study, we introduce a reproducibility-aware geometric evaluation framework designed to disentangle binding-site localization, fixed-frame pose accuracy, and run-to-run stability. Application of this framework to representative protein–ligand systems demonstrates that accurate pose generation, correct site localization, and reproducible pose ranking are separable properties. The results illustrate that pose accuracy alone does not fully characterize the practical reliability of web-based docking outputs and motivate inclusion of stability-aware metrics in docking validation workflows.Methods Five widely used web-based docking servers representing distinct algorithmic paradigms were evaluated, including a Vina-class reference implementation (MolModa), stochastic sampling platforms (GalaxyDock, SwissDock, PaRDOCK), and a geometry-driven rigid-body approach (PatchDock). Docking outputs were analyzed using complementary geometric metrics: centroid-based localization to assess binding-site placement, in-place root-mean-square deviation (RMSD) to evaluate fixed-frame pose accuracy, and aligned RMSD to quantify conformational agreement independent of spatial placement. Operational stability was assessed using triplicate docking runs under unbiased re-docking conditions. All geometric analyses were performed using custom Python script designed to ensure coordinate-frame consistency and symmetry-aware RMSD evaluation.