Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, training such agents from outcome-only rewards suffers from credit-assignment ambiguity, obscuring which intermediate tool-use decisions drive success or failure. In this paper, we propose PORTool, an importance-aware policy-optimization algorithm that reinforces agents' tool-use competence from outcome-level supervision while assigning reward at the step level. Specifically, PORTool generates a rewarded rollout tree in which trajectories share prefixes before branching, enabling direct comparisons among alternative tool-use decisions within the same context. It then estimates each step's importance by a correctness-dominant signal, i.e., whether descendants of that step can ultimately produce a correct final answer, plus an auxiliary term indicating whether the step's tool calls satisfy formatting constraints and execute successfully. Using these step-wise importance estimates, PORTool updates the policy to generate efficient tool-call steps, guided by both local comparisons within each branching decision and the overall quality of entire trajectories. Experiments show that PORTool improves final-answer accuracy while reducing tool-call steps compared with state-of-the-art policy-optimization baselines, and ablation studies confirm the robustness of the proposed step-wise importance estimates.