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' is a generic string with no format constraints, validation rules, or examples of valid payloads. No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. No error handling guidance, no idempotency markers, 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., 'search_catalog_facets', 'discover_product_filters'). Tool definition is visible in code but lacks production-grade rigor.

• Rename tool to a domain-specific verb: 'search_catalog_with_facets' or 'discover_product_facets' to match the actual skill operation and help LLMs select it correctly.
• Replace 'query_payload' string parameter with structured fields: 'semantic_query' (string), 'price_max_usd' (number, 0-10000), 'size_filter' (array of enum values), 'in_stock_only' (boolean). Document each field's purpose and constraints.
• Document the output schema explicitly: return object with fields 'search_discovery_id' (string), 'matched_products_count' (integer), 'applied_faceted_filters' (object), 'top_ranked_skus' (array of objects with sku, title, price_usd, relevance_score), 'facet_discovery_feed_url' (string).
• Expand tool description to 100-150 chars: 'Search product catalog with semantic query and dynamic facet filters (price, size, fabric). Returns ranked SKUs and facet discovery URL. Use this to find products matching user preferences with real-time inventory filtering.'
• Add error handling: document failure modes (invalid query format, no matches found, service timeout) and recovery guidance (e.g., 'If no matches, try broadening price_max_usd or removing size_filter').