This server has severe definition quality issues. The single tool 'execute_skill_action' has a vague, generic description that does not explain WHAT the tool does, WHEN to use it, or what it returns. The input schema is present but minimal, only three properties with basic descriptions. Critically, there is NO documented output schema visible in the code, making it impossible for LLMs to plan downstream actions. The tool name uses 'execute' + 'action', which is generic and does not convey the specific business intent (e.g., 'optimize_product_for_agentic_search' would be clearer). The implementation in mcp_server.py is a stub that only prints a JSON status message and does not actually handle tool invocations or parameter validation. The client.py file shows the real logic, but it is not integrated into the MCP server, the server does not call it. This is a non-functional MCP server.

• Rename tool to 'optimize_product_for_agentic_search' or 'enrich_product_catalog_for_agents' to clearly convey the business intent.
• Expand tool description to 100-150 chars: 'Optimize a product's metadata for AI agent discovery and zero-click purchasing. Returns agentic readiness score, token efficiency metrics, structured attributes, and checkout endpoint.'
• Document the complete output schema with all fields: agentic_search_readiness_score (number 0-100), llm_context_token_efficiency_pct (number), structured_attributes_schema (object with battery_life_hours, decibel_attenuation_db, codec_support array, instant_checkout_api_endpoint), agentic_seo_tags (array of strings), zero_click_buying_enabled (boolean).
• Implement the MCP server to actually handle tool invocations. Parse the 'execute_skill_action' request, call the client logic, validate inputs, and return structured responses with proper error handling.
• Add parameter descriptions: sku (string, 5-20 chars, alphanumeric + underscore), name (string, 1-255 chars, product display name), price (number, >= 0, in USD).