Partial credit for tool calling is essential for building practical AI agents and effective reward models. In real-world scenarios, agents rarely achieve perfect execution on the first try, yet an all-or-nothing evaluation approach would penalize them severely for minor mistakes, providing no signal about what they did correctly. By measuring partial success—such as calling 2 out of 4 required tools, or using correct tool names with incomplete parameters—we can give agents meaningful feedback that reflects their actual progress. This is particularly valuable for model fine-tuning and reinforcement learning, where gradual rewards create much stronger learning signals than binary success/failure metrics. When training reward models or fine-tuning agents with RLHF, partial credit helps models understand which aspects of their reasoning are correct and which need improvement, enabling them to learn incrementally rather than through trial-and-error guessing. For example, an agent that correctly identifies the right tool but uses slightly incorrect parameters should receive a higher score than one that calls entirely wrong tools, creating a gradient that guides the model toward better performance. This nuanced evaluation approach not only makes agents more robust in production environments where partial success is often sufficient, but also accelerates the training process by providing richer feedback at every step.