Single tool 'compute_distillation' has a reasonable description (145 chars) but critical schema deficiencies. Input schema is present with three parameters, all with type declarations and descriptions, meeting baseline requirements. However, NO output schema is documented anywhere, the tool returns a dict with 5 computed fields (total_loss, distillation_loss, hard_label_loss, student_top_prob, teacher_soft_entropy) but LLMs cannot see this structure in advance. Tool naming lacks action verb prefix (should be 'compute_distillation_loss' or 'evaluate_distillation'). No error handling guidance, no validation of input ranges (logits could be any float), no recovery paths. The implementation is mathematically sound but the MCP interface is incomplete.

• Add explicit output schema documentation: return type is object with fields {total_loss: float, distillation_loss: float, hard_label_loss: float, student_top_prob: float, teacher_soft_entropy: float}. Include field descriptions explaining what each metric represents.
• Rename tool to 'evaluate_distillation_step' (clearer verb) and expand description to 60-150 chars explaining: 'Computes knowledge distillation loss for a student-teacher model pair. Returns combined loss (hard label + KL divergence) and intermediate metrics for training monitoring.'
• Add parameter constraints: student_logits and teacher_logits must be equal-length arrays of floats (typically 2-1000 elements). target_label must be a valid index (0 <= target_label < len(logits)). Document in descriptions.
• Implement input validation with actionable error messages: 'Logits arrays must be equal length (got student=4, teacher=3)' or 'target_label=5 out of range for 4-class logits'.
• Extend tool description with use-case context: 'Use during model training to monitor student convergence to teacher. Call after each batch with student and teacher forward passes.'