Single tool 'execute_skill_action' has a reasonable name (verb_noun pattern) and adequate description (95 chars, within 10-1024 range). Input schema is present with 3 parameters, all typed as string/number with descriptions. Parameter descriptions are minimal (12-50 chars) and lack format/constraint details. No error handling guidance, no idempotency hints, no security considerations. The tool modifies state (credit approval decision) but description does not explicitly state this. Tool definition is visible in mcp_server.py but implementation is a stub returning hardcoded mock data, not production-ready. Overall: bare minimum structure, missing critical documentation for LLM reasoning.

• Add explicit output schema documenting all response fields: tamara_decision_id, credit_approval_status (enum: APPROVED_SHARIA_COMPLIANT, DENIED, etc.), split_in_installments, installment_amount_sar, fraud_risk_score, nafath_national_sso_verified, etc. Include types and descriptions for each.
• Revise tool description to state: 'Executes risk scoring and credit approval evaluation for BNPL checkout transactions. This tool makes irreversible credit decisions, use with caution. Returns approval status, installment plan, and fraud risk metrics.'
• Add format constraints to parameters: national_id='Format: SA_<10-digit number>', cart_amount_sar='Range: 100 - 50000 SAR', merchant_id='Format: MERCH_<alphanumeric>'.
• Implement error handling: document failure modes (e.g., 'Invalid national ID format', 'Cart amount exceeds limit', 'Merchant not found') with actionable recovery guidance.
• Add a confirmation_required field to the response or implement a separate 'confirm_credit_approval' tool to gate high-value decisions, preventing accidental approvals.