Mechanical properties of snow, such as Young’s modulus and Poisson's ratio, are used in avalanche release models but rarely measured directly in snowpits. Researchers chain published parameterizations to estimate these properties, but the combinations and their practical coverage have not been systematically compared. We present SnowPyt-MechParams, an extensible, graph-based framework that encodes parameterizations as a directed acyclic graph, enumerates valid pathways, and propagates measurement uncertainty through each one. For an example dataset, we developed SnowPylot to parse CAAML files from the SnowPilot database, structuring 50,278 snowpits (371,429 layers, 2015–2025 water years) into 14,776 slabs from Extended Column Test results with propagation. As an example application, we implemented pathways for the shear component of slab weight per unit area with and without elastic properties. The highest-coverage shear-weight pathway succeeds for 5,470 slabs (37.0 %), while the highest-coverage shear-weight-with-elasticity pathway succeeds for only 687 slabs (4.6 %), reflecting elasticity’s longer calculation chains. Slab bending stiffness, D<sub>11</sub>, carries a median relative uncertainty of 78 % (IQR: 56–85 %). Because multiple pathways can reach the same target, estimates vary substantially, with a median max/min ratio for D<sub>11</sub> of 32 (up to 63) per slab. These results demonstrate the framework's utility to systematically calculate coverage, uncertainty, and range in mechanical properties from snowpit observations using different parameterizations.