Critical deficiencies across all dimensions. The 'nomnom' tool has a description, but the input schema is drastically under-specified. The tool accepts a single 'command' parameter as a freeform string, which violates multiple core patterns: no enum constraints for valid commands, no parameter description, no documented output schema, and no error handling guidance. The description ('Universal nutrition tracking command interface...') is generic and does not explain WHEN to use the tool or HOW commands map to agent intents. No evidence of structured response documentation, pagination support, or recovery guidance. The tool essentially delegates command parsing to a subprocess without type safety or constraint validation, forcing the LLM to guess valid command syntax. Source code shows executeCommand() in src/cli.ts and mcp.ts, but actual tool registration and schema definition are not visible in the provided excerpts, making it impossible to verify proper schema implementation.

• Decompose 'nomnom' into verb-based tools with clear responsibilities: search_foods (query USDA database), log_meal (record food intake), get_meal_history (retrieve logged meals), get_nutrition_goals (fetch user targets). Each tool should have one clear action.
• For 'search_foods', document the input schema with an enum or regex pattern for search terms, a 'limit' parameter (1-100), and an 'offset' for pagination. Output should return structured results with food_id, name, calories, protein, carbs, fat per item.
• For 'log_meal', accept natural parameters (food_name, quantity, unit, meal_type, logged_at) rather than a freeform 'command' string. Document that quantity must be a positive number, unit must be one of [g, oz, cup, tbsp, tsp, serving, bowl, plate], meal_type must be [breakfast, lunch, dinner, snack].
• Document the output schema for each tool. E.g., search_foods returns {results: [{food_id, name, calories, protein_g, carbs_g, fat_g}], total_count, next_offset}. log_meal returns {meal_id, status: 'logged', logged_at, calories_logged}.
• Add error handling guidance: 'If search returns no results, try a broader term or check get_food_suggestions(). If log_meal fails with "invalid quantity", provide quantity as a decimal number (e.g., 1.5, not "one and a half").'
• Implement pagination for search_foods and get_meal_history with limit and offset parameters, returning total_count and next_offset so agents know if more results exist.
• Add tool annotations (destructiveHint for log_meal, readOnlyHint for search_foods) so agents understand which operations modify state.