Food Tracker MCP has clear, well-structured tool definitions with consistent naming (verb_noun pattern) and reasonable descriptions (avg ~120 chars). All 6 tools have input schemas with type definitions and parameter descriptions. However, output schemas are completely undocumented, LLMs cannot predict what fields to expect from responses, breaking downstream tool chaining. Error handling is absent; no guidance on recovery or retryability. Parameter descriptions lack constraints (e.g., date format validation, numeric ranges). The log_food tool accepts optional nutrition fields but doesn't validate that at least one is provided, risking incomplete logging. No pagination support despite potential for large result sets (get_daily_log, get_summary). Schemas are present but minimal, missing field-level documentation and return type specifications.

• Document output schemas for all tools. Specify field names, types, and descriptions. Example: search_food returns {foods: [{fdc_id: number, name: string, calories_per_100g: number, ...}], total_results: number}.
• Add error handling with recovery guidance. Catch API failures and return: {error: 'USDA API unavailable', retryable: true, suggestion: 'Try again in 30 seconds'} or {error: 'Invalid date format', retryable: false, suggestion: 'Use YYYY-MM-DD format'}.
• Add numeric constraints to schemas: calories (0 - 10000), protein_g/carbs_g/fat_g (0 - 500), serving_size (0.1 - 10000). Add format: 'date' to date parameters and validate in code.
• For log_food, require at least one nutrition field (protein_g, carbs_g, fat_g, or fiber_g) alongside calories. Return error if all are omitted: 'At least one macro (protein, carbs, fat, or fiber) required.'
• Add pagination to get_daily_log and get_summary: accept limit (1 - 100, default 20) and offset parameters. Return {entries: [...], total: number, has_more: boolean} to enable multi-page queries.