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. No error handling guidance, no parameter validation rules, and no indication of what 'deterministic, zero-dependency' means operationally. The tool name uses a generic verb ('execute') rather than domain-specific action (e.g., 'plan_goal_basket', 'calculate_bundle'). Tool definition is visible in code but lacks production-grade rigor.

• Rename tool to a domain-specific verb: 'plan_goal_basket' or 'calculate_bundle_items' to signal intent and enable LLM selection without reading the full description.
• Document the output schema explicitly: define the structure of the response (e.g., basket_plan_id, planned_bundle_items array with category/title/price/priority fields, total_cost, budget_remaining). Include field types and descriptions.
• Expand parameter 'query_payload' into explicit, typed parameters: 'goal_description' (string, 50-500 chars), 'budget_ceiling_usd' (number, 10-10000), 'priority_filter' (enum: MANDATORY|RECOMMENDED|OPTIONAL). Remove the opaque payload string.
• Add error handling: return structured errors with actionable guidance. E.g., 'Budget too low: minimum $150 required for this goal. Try increasing budget_ceiling_usd or reducing goal scope.'
• Enhance tool description to 150-250 chars: 'Plan a multi-item shopping basket to achieve a specific goal within a budget. Recommends items by category (Shelter, Sleep System, Cookware, etc.) with priority levels. Returns a basket plan with total cost and remaining budget. Use this to help users assemble goal-driven shopping bundles.'