Tool-augmented language models are increasingly applied to financial document reasoning, a setting that demands numerical precision over long financial filings. Earlier comparisons either mix up the model with its scaffolding or report tool-use gains without separating code, strategy, and environment. We isolate these factors in a controlled same-model ablation: six configurations share an identical GPT-5 backbone and differ only in the tool surface (callable tools plus their descriptions), with one control that additionally forces sub-model delegation. On DocMath-Eval under oracle context, the fully equipped configuration beats the restricted-tool baseline by +3.6 points (p=0.0028). Where this gain comes from matters more than its size: the system tools that give that configuration its name are used on under 0.2% of samples, and a description-only control, which describes the same tools but never runs them, performs almost identically (within 1 point on every split). The benefit comes from the tool descriptions, not from executing the tools. On long-document splits, the equipped model uses fewer iterations, has answers available earlier when runs are cut short, and varies its tool mix with document length. Comprehension errors dominate the hardest failures: the bottleneck is document comprehension, not the tool interface.