This paper introduces the Morphological Participation Index (MPI), a substrate-agnostic framework for estimating whether a system’s morphology can plausibly support strongly integrated, coherence-sensitive, trace-rich, and temporally scaffolded dynamics. “Participation” refers to the degree to which morphology actively contributes to, constrains, and scaffolds the integrated, trace-bearing, and temporally organized dynamics available to a system. The immediate motivation comes from two adjoining lines of work: spectral approaches to resistance to decomposition, and recent proposals by Schneider and Bailey concerning prototime, quantum Darwinist stabilization, and the selective emergence of conscious basins [2,16–18]. MPI evaluates the structural conditions under which a system might sustain unified dynamics, stable internal traces, and organized temporal regimes, without presupposing a human, cortical, or even purely biological baseline. Formally, MPI represents morphology as a weighted constraint hypergraph [4,24], or as an explicit multilayer family of such hypergraphs [11], and returns a score bundle rather than a single undifferentiated scalar. The core bundle consists of six components: integration geometry, multiscale nesting, resonant-mode support, trace geometry, temporal scaffolding, and robustness. An optional contextual patchiness module is provided for domains in which a defensible predicate family is available. The integration component is anchored in a balanced-cut spectral formalism: it uses sweep cuts over the Fiedler vector of the normalized Laplacian rather than raw minimum-cut objectives or simple sign cuts, thereby avoiding familiar degeneracies and linking MPI directly to contemporary spectral proxies for resistance to informational decomposition [6,19,23]. The principal contribution of MPI is a structural profile: seam maps, multiscale partitions, trace-capacity maps, temporal breadth measures, and perturbation-stability diagnostics, in a form that remains useful across biological, artificial, collective, and other nonstandard architectures. More generally, the same diagnostics may be useful in AI alignment. Seam topology, trace geometry, and temporal scaffolding provide a way to screen for architectures that may be difficult to audit, prone to distributed lock-in, or vulnerable to hidden coordination through narrow bottlenecks or persistent externalized traces. MPI can also serve as a screening tool for artificial systems whose structural profile merits closer safety and oversight attention.