Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and violates verb_noun clarity (pattern:tool). Description is present but vague (67 chars, below 100-char minimum for clarity). Input schema exists but parameter 'query_payload' lacks semantic meaning, it's a catch-all string with no format constraints, enums, or validation guidance. No output schema documented. Tool appears to be a pass-through wrapper with no real validation or error handling. No evidence of idempotency, permission gating, or recovery guidance.

• Rename tool to 'diagnose_merchant_sales_velocity' or 'analyze_sales_cohort_performance' to reflect actual domain action and enable LLM intent inference.
• Replace 'query_payload' string parameter with explicit, typed parameters: 'date_range_days' (integer, 1-365), 'tracking_channels' (array of enum: 'Shopify Direct', 'Amazon US', 'TikTok Shop'), 'cohort_segment' (optional enum). Document each parameter's purpose and constraints.
• Document output schema with typed fields: 'diagnostics_run_id' (string), 'total_aggregated_gmv_usd' (number), 'fastest_growing_channel' (string), 'flagged_performance_anomalies' (array of objects with 'channel', 'sku', 'issue'), 'channel_breakdown_matrix' (array), 'executive_dashboard_url' (string, URI format).
• Expand tool description to 120-180 chars: 'Analyze merchant sales velocity across cohorts and channels. Returns GMV aggregates, growth rates, performance anomalies, and channel breakdowns. Use this to diagnose sales performance issues and identify growth opportunities.'
• Add error handling: validate date_range_days is 1-365, validate channels are from allowed enum, return structured error with constraint violation details if invalid. Return partial results if enrichment fails (e.g., missing anomaly detection).