Multi-turn tool calling is a core evaluation scenario for large language model (LLM) agents. On public tool-calling benchmarks, open-weight models now approach or even surpass closed-source frontier models in aggregate accuracy. However, aggregate accuracy averages over many different multi-turn situations and obscures whether progress is balanced across them, masking failure redistribution. We propose an action-class-oriented diagnostic framework that decomposes failures into two orthogonal dimensions: capability and calibration. The framework operates over a four-class action space (tool_call/ask/refuse/confirm) and introduces a self-revealing upper bound, Acc ≤ GAR (Gold Action Recall), whose two violation directions separately localize action-class miscalibration and sub-action failures. We validate it on an extensive panel of tool-calling models that includes both open-weight families and closed-source frontier systems, across multiple multi-turn tool-calling benchmarks for cross-benchmark robustness. Across our panel the bottleneck is calibration, not capability: every model can produce all four classes, but which one it fires in which situation is highly model-specific. On no-tool-call scenarios, a top-ranked family’s GAR is roughly one-quarter that of size-matched peers, a 64.5 percentage point (pp) gap, indicating its ranking reflects benchmark-distribution fitting rather than genuine in-context calibration. We further show the calibration profile can be reshaped through context manipulation alone, with the reshaping direction strongly model-specific. We recommend that multi-turn tool-calling evaluations report per-category (GAR, tool-call-emit rate, Acc) triples, exposing the calibration structure aggregate accuracy hides. Our framework provides a falsifiable, reproducible diagnostic foundation for evaluation protocol design, training objective alignment, and inference-time calibration research.