Single tool with minimal documentation. Tool name lacks action verb (should be 'evaluate_kto_loss' or similar). Input schema present with proper types but no parameter descriptions. No output schema documented. No error handling guidance. No security considerations. Composition is simple (one tool, one job) but severely underdocumented for LLM consumption.

• Rename tool to 'evaluate_kto_loss' or 'compute_kto_loss' to start with an action verb and clarify intent.
• Add descriptions to all three parameters: 'policy_logprob: Log probability of the policy model output (float, typically negative)', 'ref_logprob: Log probability of the reference model output (float, typically negative)', 'is_desirable: Boolean flag indicating whether this sample represents desirable (thumbs up) or undesirable (thumbs down) feedback'.
• Expand tool description to 150-200 chars explaining: what KTO loss is, when to call it (e.g., during training evaluation), and what the output fields mean. Example: 'Evaluate Kahneman-Tversky Optimization (KTO) loss on unpaired binary feedback. Use during training to compute loss for desirable (thumbs up) and undesirable (thumbs down) samples. Returns implicit reward, utility, KTO loss, and satisfaction probability.'
• Document the output schema in the tool description or as a separate schema field: '{implicit_reward: float, utility: float, kto_loss: float, is_desirable: float (0 or 1), satisfaction_probability: float (0-1)}'.
• Add input validation and error handling: catch non-numeric logprobs, return actionable error like 'Invalid policy_logprob: expected float, got {type}. Example: -2.5' instead of a stack trace.