Single tool 'execute_skill_action' has a reasonable schema with typed parameters and defaults, but critical gaps undermine quality. Description is only 67 chars, below the 194-char baseline and lacks WHEN/WHY context. Parameter descriptions are present but generic (e.g., 'Activity level of the pet' without enum constraints). No output schema documented, LLMs cannot plan downstream calls. No error handling guidance. Tool name uses 'execute' (generic verb) rather than domain-specific action like 'calculate_meal_plan'. The server's mcp_server.py is a stub that prints JSON and exits, no actual MCP protocol implementation (no request/response loop, no tool invocation handler). This is a proof-of-concept, not a functional MCP server.

• Rename tool to 'calculate_pet_meal_plan' (verb_noun pattern, domain-specific).
• Expand description to 150 - 250 chars: 'Calculates a customized daily meal plan for dogs based on weight, activity level, and allergies. Returns recommended recipe, portion size, caloric target, and subscription cost. Vet-approved nutrition.'
• Add output schema documenting all returned fields: pet_profile (object), daily_caloric_target_kcal (number), recommended_recipe (string), portion_grams_per_day (number), subscription_frequency_days (number), monthly_cost_usd (number), vet_nutritionist_approved (boolean).
• Convert 'activity_level' to an enum: ['low', 'moderate', 'high', 'very_active'] with description 'Pet activity level; determines caloric multiplier.'
• Implement actual MCP protocol in mcp_server.py: read JSON-RPC requests from stdin, dispatch to tool handlers, write responses to stdout. Use a library like mcp-python or implement the spec directly.