Single tool 'execute_skill_action' has critical definition gaps. Description is generic (89 chars, below 194-char baseline) and lacks WHAT/WHEN/WHY context. Input schema exists but parameter 'query_payload' has only a 47-char description, no format guidance, constraints, or examples of valid payloads. No output schema documented; response is hardcoded mock JSON with no field descriptions. Tool name uses 'execute' (generic verb) rather than domain-specific action like 'score_fraud_risk' or 'assess_return_propensity'. No error handling guidance, no parameter validation rules, no idempotency guarantees. The actual fraud-scoring logic is hidden in client.py (not exposed via MCP), making the tool a hollow pass-through.

• Rename tool to 'score_return_fraud_propensity' or 'assess_wardrobing_risk' to reflect actual domain action and improve LLM intent inference.
• Expand tool description to 150 - 250 chars: 'Assess pre-purchase return fraud risk for a shopper based on cart items and historical return rate. Returns fraud propensity score (0 - 1), risk tier (LOW/MEDIUM/HIGH), and recommended checkout policy. Use this before finalizing high-value orders.'
• Document input schema: define 'query_payload' as a JSON string with explicit structure (shopper_id, cart_items array with sku/qty/price, historical_return_rate_pct). Add format constraint and example.
• Document output schema with field descriptions: fraud_propensity_score (float 0 - 1), risk_tier (enum: LOW_FRAUD_RISK_RELIABLE, MEDIUM_FRAUD_RISK_CAUTION, HIGH_FRAUD_RISK_BLOCK), wardrobing_suspicion_flag (boolean), recommended_checkout_policy (enum), fraud_evaluation_report_url (string URI).
• Add error handling: return structured errors with recovery guidance (e.g., 'Invalid shopper_id format. Expected format: usr_[0-9]+') and classify as retryable vs. user-fixable.