OrderFood has 12 well-named tools with consistent verb_noun patterns (search_restaurants, get_restaurant, add_to_cart, place_order, etc.). All tools have descriptions (avg ~100 chars, within baseline 34 - 392). Input schemas are present with enums for platform selection. However, parameter descriptions are sparse or missing: 'restaurant_id', 'item_id', 'order_id', 'address_id', 'payment_method_id' lack descriptions entirely. Output schemas are not documented, LLMs cannot infer what fields to expect from responses. Error handling is minimal; no recovery guidance or actionable error messages visible. The place_order tool (IRREVERSIBLE risk) lacks confirmation/dry-run pattern. Overall: solid naming and basic structure, but incomplete parameter docs and missing output schemas prevent higher scores.

• Document output schemas for all tools. Example: 'search_restaurants returns {restaurants: [{id, name, rating, delivery_time_min, delivery_fee, cuisine, image_url}], total_count}'. This enables agents to chain calls and extract the right fields.
• Add descriptions to all ID parameters. Example: 'restaurant_id: string, unique identifier returned by search_restaurants or get_restaurant'. This clarifies where IDs come from and prevents type confusion.
• Implement confirmation pattern for place_order. Add a 'dry_run' boolean parameter or a separate 'preview_order' tool that returns estimated totals without committing. Agents need to verify before irreversible actions.
• Document error scenarios and recovery paths. Example: 'If auth_error is returned, call ping_platform to diagnose. If rate_limited, retry after 60 seconds.' This guides agent behavior on failures.
• Clarify add_to_cart option selection. Document: 'options array must include one selection per required group (from get_restaurant.categories[].items[].option_groups). Omitting a required group returns validation_error.' Include example: [{group_id: 'size', option_id: 'large'}].