Single tool 'optimize' has moderate naming clarity but critical gaps in schema documentation and parameter descriptions. The tool definition exists in client.py with a docstring, but the actual MCP server registration cannot be verified from the provided source. Input schema shows types (list[dict], Optional[dict], Literal) but lacks comprehensive descriptions for nested dictionary fields. Output schema is completely undocumented, the method returns a complex dict with 'analysis', 'policy_score', 'current_policy', 'optimized_policy', 'estimated_impact', 'business_goal' keys, but no structured schema is provided to guide LLM usage. Parameter descriptions are present but lack detail on valid ranges, constraints, and format specifications. No error handling guidance is visible.

• Document complete output schema: specify structure of 'analysis' object (total_refunds, approval_rate, top_reasons array with reason/count/pct fields, etc.), 'policy_score' as float 0-100, 'optimized_policy' dict with return_window_days/restocking_fee_pct/free_returns/exchange_allowed/recommendations array, 'estimated_impact' dict with retention_rate_change/conversion_rate_change/estimated_cost_change_usd/customer_satisfaction_impact strings.
• Expand current_policy parameter description to document each field: 'return_window_days (int, 1-365, default 30): number of days from purchase for customer-initiated returns', 'restocking_fee_pct (float, 0-100, default 0): percentage fee on successful returns', 'free_returns (bool, default True): whether customer bears shipping costs for returns', 'exchange_allowed (bool, default True): whether customer can exchange for different size/color instead of refund'.
• Specify refund_records validation rules: 'Each dict must include amount (float, >0), reason (str, non-empty), days_since_purchase (int, >=0), approved (bool). Missing fields default to: amount=0, reason='unknown', days_since_purchase=0, approved=True. Records with amount<=0 are skipped with a warning in analysis.'
• Add error handling responses: document scenarios like 'Empty records list: returns analysis with all zeros', 'Invalid amounts (negative/NaN): returns error with example valid record structure and recoverable fields', 'Missing required keys: returns error with field list and suggestion to validate input with check_refund_data tool (if created)'.