Single tool 'execute_skill_action' has a minimal description (58 chars) that lacks WHAT, WHEN, and actionable context. Input schema exists but is extremely sparse: only one parameter (batch_qr_code) with a generic string type and no format/pattern constraints. No output schema is documented anywhere in the codebase. The tool name uses 'execute' + 'skill_action', a generic verb pair that does not clearly convey the specific action (audit, trace, validate). No error handling guidance, no parameter validation rules, no recovery paths. The implementation in mcp_server.py is a stub that prints JSON but does not actually invoke the client logic. This is a proof-of-concept skeleton, not a production-ready tool.

• Rename tool to 'audit_produce_traceability' to match the actual domain action and improve LLM intent inference.
• Expand description to 100 - 200 chars: 'Audits farm-to-door produce traceability by scanning a batch QR code. Verifies cold chain compliance (target 4°C), harvest certification, ozone sanitization, and pesticide residue levels. Returns traceability ID, crop name, farm source, quality grade, and compliance status.'
• Document the complete output schema in the tool definition: {traceability_id: string, crop_name: string, farm_source: string, harvest_timestamp: ISO8601, ozone_wash_and_sanitization_certified: boolean, chemical_pesticide_residue_ppm: number, pan_scanned_quality_grade: enum[GRADE_A_EXPORT_READY, ...], cold_chain_maintained_celsius: number}.
• Add format constraint to batch_qr_code parameter: 'QR code identifier (format: PLK_SCAN_<CROP>_<NUMBER>, e.g. PLK_SCAN_TOMATO_091)' and validate against regex pattern in implementation.
• Implement error handling that returns structured errors with recovery guidance: 'Batch not found' → 'Verify QR code and rescan'; 'Cold chain broken' → 'Contact farm for replacement batch'; 'Invalid QR format' → 'Ensure QR code matches PLK_SCAN_* pattern'.