Metamer mismatch bodies (MMBs) quantify the extent of metamer mismatching for a given color stimulus with a change in color mechanism (i.e. change in illuminant and/or observer). Prior work has shown that the MMB boundary can be efficiently approximated by spherical sampling of unit directions in the 6D joint color-mechanism space, and for each sampled direction, maximizing the boundary point subject to the metameric cross-section constraints. Many sampled directions map to the same boundary vertex, so the number of recovered vertices is typically far smaller than the number of sampled directions. This produces a plausible approximation, but the resulting boundary vertices, expressed in sensor-response spaces (for example XYZ, LMS, or RGB), are often distributed in a highly non-uniform manner. Increasing the number of sampled directions increases the number of recovered vertices but does not improve boundary uniformity. We explored a simple post-processing workflow that builds a larger candidate pool of vertices and then selects a fixed-size subset using a spacing-driven sampling algorithm, improving vertex uniformity as measured by a nearest-neighbor metric. This approach substantially improves vertex uniformity in sensor space, but it can discard boundary-defining extreme vertices, potentially altering hull volume and other distinguishing boundary features. We therefore argue that any practical workflow for improving MMB vertex uniformity should include an explicit mechanism for retaining boundary-critical extremes prior to applying spacing-driven selection.