LLM-based agents increasingly use multiple external tools to complete complex tasks. We study Tools Orchestration Privacy Risk (TOP-R): an agent may combine individually non-sensitive tool returns and disclose an unintended sensitive conclusion. We formalize TOP-R with three conditions: conclusion sensitivity, single-source non-inferability, and compositional inferability. We introduce LRSE (Library-Grounded Reverse-Inference Seed Expansion), a four-library reverse-construction pipeline grounded in privacy norms, reasoning chains, tool schemas, and task scenarios, and use it to build TOP-Bench, a 1,000-instance benchmark. The benchmark evaluates final-response semantic disclosure under a controlled two-stage tool-use protocol. Across six LLM agents, task completion remains high, but the average leakage rate reaches 88.6 percent, yielding an H-score of only 20.4. Two prompt-only safeguards improve H-score by about 2.7 points on the main benchmark. We further propose TOP-Align, an SFT+DPO post-training method for safer task completion boundaries. On a separate post-training evaluation split, TOP-Align improves H-score by 16.2 points over the corresponding base model, compared with a 4.9-point average gain from prompt-only mitigation on the same split. These results show that TOP-R requires mitigation beyond prompting alone.