Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context for LLM selection. Input schema is present but parameter 'query_payload' has only a generic description ('Input execution payload for this skill') with no format, constraints, or examples. No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. The tool name uses a generic verb ('execute') rather than a domain-specific action verb. No error handling, no parameter validation guidance, and no indication of what 'deterministic, zero-dependency' operations actually entails. Tool definition is visible in code but lacks production-grade rigor.

• Rename tool to a domain-specific verb: 'forecast_sprint_velocity' or 'predict_cycle_completion' to match the actual skill purpose and help LLMs select it correctly.
• Expand tool description to 100-200 chars explaining: (1) What it does (forecasts sprint velocity based on historical data), (2) When to use it (when planning a sprint cycle), (3) What it returns (forecast with confidence and alerts).
• Document the output schema explicitly: define fields like 'forecast_id', 'cycle_name', 'planned_points', 'average_historical_velocity', 'completion_probability_pct', 'scope_creep_alert', 'recommended_point_trim', 'burndown_chart_url' with types and descriptions.
• Replace free-form 'query_payload' string with structured parameters: 'cycle_name' (string), 'planned_story_points' (integer, 1-500), 'completed_points_past_3_cycles' (array of integers). Add descriptions and constraints for each.
• Add error handling: return distinct error responses for invalid input (e.g., 'planned_story_points must be a positive integer'), missing data (e.g., 'completed_points_past_3_cycles requires at least 1 historical data point'), and service failures with actionable recovery guidance.