Section 3.2 defines the multiplier as the “Copilot Multiplier”, but elsewhere the document describes a generic “Model Multiplier” (and the concept is model-relative, not Copilot-specific). This terminology mismatch can confuse implementers and API consumers; consider standardizing on “Model Multiplier” (and, if needed, note that the JSON field name remains copilot_multiplier).

raw_total_tokens is defined in Section 4.1 as a per-invocation value, but Section 5.2 reuses the same identifier for the aggregated total across invocations. Reusing the same symbol for two different quantities makes the spec ambiguous; consider renaming one (e.g., raw_total_tokens_i vs raw_total_tokens_total) or explicitly distinguishing per-invocation vs aggregated totals in the formulas.

The summary object requires a base_weighted_tokens field, but the spec never defines how to aggregate base-weighted tokens across multiple invocations (Section 5 defines ET_total and raw_total_tokens, but not base_weighted_tokens_total). Add a normative definition (e.g., base_weighted_tokens_total = Σ base_weighted_tokens_i) so implementations can produce consistent summaries.

Section 6.1 states each invocation node MUST include a derived object with base_weighted_tokens and effective_tokens, but the worked example input data omits derived for every invocation. Either update the example to include the required derived fields, or relax the schema (e.g., make derived OPTIONAL at lower conformance levels / when only raw usage is provided).

Section 7 says a conforming response MUST include both a summary object and an invocations array, but Appendix A.4’s output example only shows summary. If the example is meant to be conforming, include the invocations array (or explicitly label it as a partial/minimal example and align that with the conformance requirements).

Section 8.4 says invocation nodes with incomplete data “SHOULD be flagged”, but the node schema doesn’t define any field/mechanism for representing this flag (and Section 9 extensions don’t specify a recommended shape). Consider adding a standard field (e.g., derived.incomplete, flags, or missing_fields) so different implementations don’t invent incompatible representations.

In Appendix B’s default-weights formula, terms like 0.1 C_i / 4 O_i rely on implied multiplication, which is easy to misread in a normative formula section. Consider using explicit multiplication (e.g., 0.1 × C_i, 4 × O_i, 4 × R_i) to match the earlier formula style and reduce ambiguity.