This server has severe definition quality issues. The single tool 'execute_skill_action' lacks a proper MCP tool registration pattern. The source code shows only a mock server that prints JSON and a client class, there is no actual MCP tool definition visible with schema registration, input validation, or output documentation. The tool description is generic and does not explain WHEN to use it or WHAT it returns. Parameters have descriptions but lack type constraints (enums for categorical values like skin_tone, eye_color, makeup_style). No error handling, no output schema documentation, and no evidence of proper MCP protocol implementation. The server appears to be a proof-of-concept rather than a production-ready MCP implementation.

• Implement proper MCP tool registration with explicit schema definition. Use the MCP SDK to register 'execute_skill_action' with a complete JSON Schema input object and documented output schema.
• Convert string parameters to enums. Define skin_tone, eye_color, and makeup_style as enum constraints with valid values (e.g., skin_tone: ['light_warm', 'medium_olive', 'deep_rich', ...]) to prevent hallucinated inputs.
• Document the output schema explicitly. Specify that the tool returns {curated_bag_id: string, subscriber_profile: object, box_products: array of {brand, item, category, value_usd}, total_retail_value_usd: number, monthly_subscription_price_usd: number, custom_choice_item_selected: string}.
• Expand tool description to 100-200 characters explaining purpose, when to call it, and what it returns. Example: 'Curate a personalized beauty subscription box based on customer skin tone, eye color, and makeup style. Returns a curated bag with 5 products, total retail value, and monthly price.'
• Add error handling with actionable recovery messages. If parameters are invalid, return 'Invalid skin_tone: got "unknown_tone", must be one of: light_warm, medium_olive, deep_rich, etc.' to enable LLM self-correction.