K-Beauty MCP has 9 tools with basic schemas and descriptions, but significant gaps prevent production use. All tools have descriptions (10-150 chars, mostly adequate) and input schemas with types. However, NO output schemas are documented anywhere, critical for LLM planning. Parameter descriptions are present but often generic ('Name of the K-Beauty brand' lacks format/constraint guidance). No error handling guidance, no recovery paths, no actionable error messages. Tool names follow verb_noun convention (search_, analyze_, get_, recommend_, check_) which is good. Composition is reasonable, each tool has one responsibility. But missing output documentation, lack of parameter constraints (enums where appropriate), and zero error handling drop this to D-range.

• Document output schema for every tool. Example: search_kbeauty_brands should return {brands: [{name, origin, founded, category, price_range, key_ingredients, popular_products}], total_count, next_cursor}. This enables LLM planning.
• Add error handling guidance to each tool description. E.g., 'Returns 404 if brand not found, try search_kbeauty_brands() with a partial name. Returns 400 if search_type is invalid, must be one of: general, products, ingredients, reviews.'
• Constrain free-form string parameters with format hints or enums. E.g., skin_description should note 'Describe in 10-500 characters'; skin_zones should document valid values like 't_zone', 'cheek_area', 'eye_area', 'forehead', 'chin'.
• Add pagination to search and list tools. E.g., search_products should accept limit (1-50, default 20) and offset/cursor parameters, and return total_count and next_cursor in response.
• Provide the actual tool implementation (kbeauty_mcp.py with @server.list_tools() and @server.call_tool() handlers). Current source shows only data files and stub main.py, cannot verify tool registration, schema validation, or output formatting.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry