Omakase courses at a sushi counter in Akasaka, Tokyo, with a pre-filled link to the booking page.
Scored across 4 tools
Each tool targets a distinct action: fetching a booking link, retrieving a specific FAQ topic, listing all courses, and recommending a course. Any overlap in output, such as recommend_course also showing other courses, does not create real ambiguity.
All four tools follow a consistent verb_noun pattern: get_booking_link, get_restaurant_info, list_courses, and recommend_course. The naming is predictable and uniformly styled.
Four tools is well-scoped for a restaurant assistance server, covering the essential guest journey: restaurant info, course options, recommendations, and booking handoff. No tools feel redundant or missing enough to inflate the count.
The set covers the core workflows of gathering restaurant information, understanding courses, getting a recommendation, and arriving at the booking page. Availability and opening hours are not directly exposed, but they are intentionally delegated to the external booking page, so agents can still work around the gap.
Link to the restaurant's TableCheck booking page with the course, date and party size already selected. The guest picks a time there, agrees to the restaurant's notice, enters their name and contact, and confirms. This tool never books, never takes payment and does not know which times are free. Refuses dates the restaurant has told us it is closed.
Inputs: date (required), course (required), people (required), lang.
The restaurant's own answer (from its FAQ) about one topic: price, reservation, duration, seating, tea_room, allergies, english, payment or location. Opening hours, lunch and days off are not answered here: point the guest to the booking page.
Inputs: topic (required), lang.
The three courses with price per person and what is included. Prices include tax and service charge.
Inputs: lang.
Suggest a course from the budget per person and whether the guest wants sake. Returns the suggestion with a reason, plus the other courses.
Inputs: lang, wants_sake, budget_per_person_jpy.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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