Multi-step tool planning is vulnerable to structural errors that propagate across dependent calls: a wrong argument, broken dependency, or mis-ordered operation can invalidate an otherwise plausible trajectory. We introduce FORTE, a framework that makes these failure modes explicit training targets. FORTE defines a nine-error taxonomy for multi-step plans, constructs taxonomy-grounded contrastive corpora by injecting minimally edited failures into gold trajectories, and trains a lightweight judge to recognize and score these errors. The same frozen judge then provides dense reward for GRPO planner optimization and verification for Best-of-N inference. Across ToolHop and NESTFUL, FORTE achieves the highest Judge Success Rate across all six open-weight backbone-dataset configurations and the highest observed end-task execution accuracy among the evaluated methods in all six settings. With Qwen2.5-7B, FORTE reaches 96.6 JSR and 99.5 dependency accuracy on ToolHop, and 98.6 JSR and 89.0 dependency accuracy on NESTFUL. Under a matched five-sample selection budget, FORTE-Qwen2.5-7B also outperforms GPT-5.5 across JSR, functional match, parameter accuracy, and dependency accuracy on both benchmarks. Ablations show that taxonomy-grounded contrastive negatives are essential for both judge discrimination and effective inference-time selection. These results demonstrate that explicitly learning which planning failures to avoid provides a strong supervision signal for robust multi-step tool planning.