MCP server

Gondola Award Travel Search Official

ai.gondola/gondola

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

Compare cash vs points on hotels, flights, and cars. Checkout supported hotels and rental cars.

Website Source

At a glance

Type

MCP server

Publisher

gondola.ai (verified by the MCP Registry)

Status

Working at last check

Tools

49

Token cost

About 15,639 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http gondola https://mcp.gondola.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.gondola]
url = "https://mcp.gondola.ai/mcp"

Gemini CLI

{"mcpServers":{"gondola":{"httpUrl":"https://mcp.gondola.ai/mcp"}}}

Cursor

{"mcpServers":{"gondola":{"url":"https://mcp.gondola.ai/mcp"}}}

VS Code

{"servers":{"gondola":{"type":"http","url":"https://mcp.gondola.ai/mcp"}}}

Antigravity

agy mcp add gondola https://mcp.gondola.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"gondola","server_url":"https://mcp.gondola.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"gondola","server_url":"https://mcp.gondola.ai/mcp"}

Tools

• book_flight ~738 tokens
  Book a prepared flight only after the traveler explicitly confirms the exact current quote, total, currency, and consent version. Use the handoff_id and quote_id returned by prepare_flight_booking and a saved payment_id from get_payment_methods. Gondola derives the traveler ID and type and a stable
• book_hotel ~416 tokens
  Book a hotel room using a rate from get_hotel_rates. Requires guest details and a saved payment method. Use get_payment_methods to find the payment ID. The gondola_rate_id comes from the room rates returned by get_hotel_rates.
• book_hotel_v2 ~432 tokens
  Book a hotel room using a rate from get_hotel_rates. Requires guest details and a saved payment method. Use get_payment_methods to find the payment ID. The gondola_rate_id comes from the room rates returned by get_hotel_rates. Returns a versioned structured result for MCP Apps.
• book_vehicle ~468 tokens
  Book a rental vehicle from a search result. Requires a Gondola account and a saved payment method (use get_payment_methods to find the payment ID). The vendor_code, rate_code, acriss_code, and search_id all come from search_vehicles results.
• cancel_flight_booking ~687 tokens
  Preview flight cancellation first with confirm=false or omitted. Show the returned itinerary cancellation scope and exact terms before asking the traveler to confirm. A void preview means Gondola will first try to void every exact ticket document and will cancel the itinerary only after complete voi
• cancel_vehicle_booking ~151 tokens
  Cancel an existing vehicle booking by its Gondola booking ID. Use this when a user wants to cancel a rental they previously booked.
• compare_rates ~334 tokens
  Compare current canonical cash vs points rates side-by-side for one or more hotels. Use this whenever the user is making a cash-vs-points decision — whether focused on a single hotel ("compare cash and points at Park Hyatt Tokyo") or weighing top picks against each other ("which of these is the best
• compare_rates_v2 ~350 tokens
  Compare current canonical cash vs points rates side-by-side for one or more hotels. Use this whenever the user is making a cash-vs-points decision — whether focused on a single hotel ("compare cash and points at Park Hyatt Tokyo") or weighing top picks against each other ("which of these is the best
• create_rate_alert ~168 tokens
  Create a rate alert to monitor a hotel for price drops. The user will be notified by email when the rate drops. Optionally specify dates, or leave them out to monitor any two-night stay.
• credit_card_coverage ~275 tokens
  Look up rental car CDW/LDW (collision damage waiver / loss damage waiver) coverage provided by a credit card. Useful before booking — tells the user whether they can decline the rental company's expensive insurance. Provide EITHER credit_card_product_name (exact card match) OR card_number_bin + card
• delete_rate_alert Deletes data · ~80 tokens
  Delete a rate alert to stop monitoring a hotel for price drops.
• diagnose_rates ~279 tokens
  Diagnose rate availability and per-supplier statuses for a specific hotel. Use this when a power user asks why certain rates aren't showing (e.g. "why is there no AAA rate?" or "is this hotel mapped to Travelport?"). Returns per-source status and a full breakdown of every rate by type. Diagnostic —
• get_booking ~216 tokens
  Get details for a hotel booking made through Gondola, using the Gondola booking ID shown as "Gondola booking" by get_upcoming_trips and get_past_trips. Returns hotel name, dates, room type, rate, status, and cancellation policy. A hotel or airline confirmation number will not resolve. Reservations G
• get_booking_link ~273 tokens
  Get a booking link for a hotel on Gondola. Use this when a user wants to book a specific hotel. Returns a URL to the hotel's booking page with dates pre-filled. Pass gondola_rate_id when a specific rate is known so the link deep-links straight to that rate's checkout page.
• get_favorite_hotels ~225 tokens
  Get the hotels the user has saved as favorites in Gondola, newest first. Use this when the user asks about their favorite or saved hotels, or to ground recommendations in places they already like. Returns each hotel's name, hotel_id, chain, address, and the date it was saved. Pass the returned hotel
• get_flight_booking ~241 tokens
  Get the current owner-scoped state of a Gondola flight booking, including booking, payment, ticketing, action-required, cancellation, ticket settlement, refund-review, failure, and chronology state. Ticket settlement is separate from refunds, customer payouts, credits, and Gondola Cash. Use this aft
• get_flight_credits ~146 tokens
  Get the user's active airline flight credits, eCredits, travel funds, vouchers, and travel-bank balances. Returns the airline, remaining usable amount, currency, credit type, and expiration date when known. Use this whenever the user asks whether they have unused airline money, credits from cancelle
• get_flight_points ~386 tokens
  Collect award (miles/points) pricing for a flight search that reported pending_sources. Call this after search_flights when you set points: true and the response came back with pending_sources — those are the loyalty programs still being scraped. Returns per-program results: cash itineraries paired
• get_flight_seats ~368 tokens
  Get current optional seat maps for the exact handoff_id and quote_id returned by prepare_flight_booking. Only approved personal supplier test accounts are currently supported. Before recommending a seat, call get_traveler_context for stated/learned preferences and get_past_trips for historical seat
• get_free_night_credits ~307 tokens
  Get the user's free night certificates — the award nights earned from credit cards and elite status, such as a Marriott Free Night Award, a World of Hyatt free night, a Hilton free night reward, an IHG reward night, or a Wyndham Go Free night. Returns each certificate's program, how many remain, wha
• get_hotel_details ~230 tokens
  Get stored property information for a specific hotel: address, chain and brand, property type, star and guest ratings, all-inclusive and adults-only flags, themes, description, check-in and check-out times and instructions, amenities, photo links, room types with beds, size, max adults, and views, p
• get_hotel_details_v2 ~246 tokens
  Get stored property information for a specific hotel: address, chain and brand, property type, star and guest ratings, all-inclusive and adults-only flags, themes, description, check-in and check-out times and instructions, amenities, photo links, room types with beds, size, max adults, and views, p
• get_hotel_rates ~573 tokens
  Get live room rates for one hotel and stay window. Rooms are grouped by type, and every room type is shown, in full while the response stays within a size budget (at most 50), with every rate each: the cheapest non-refundable, cheapest refundable, and cheapest points award are labeled first, then ev
• get_hotel_reviews ~133 tokens
  Get guest reviews for a specific hotel. Use this to help a user understand what other guests thought about a property — common complaints, recurring praise, recent service issues. Returns up to 10 recent reviews with ratings and comments.
• get_hotel_stats ~265 tokens
  Get pricing percentile context for a specific hotel. Returns where the current rate falls relative to the property's typical pricing (e.g. "this rate is in the 20th percentile — cheaper than 80% of historical observations"). Use this to add objective context to recommendations.
• get_loyalty_accounts ~144 tokens
  Get the user's loyalty program memberships and current point balances. Use this to personalize recommendations (e.g. "with 120k Bonvoy points, this Sheraton is a great points redemption") and to suggest portfolio optimizations. This covers points balances and elite tiers only — free night certificat
• get_multi_night_rates ~280 tokens
  Get cached, partial rate observations for a hotel over a flexible date range. Use this for seasonality shape and to shortlist candidate check-in dates (e.g. "any week in May"), never to prove a missing date is unavailable. `nights` is the exact stay length and `end_date` is the latest checkout, not
• get_past_trips ~430 tokens
  Get the user's past hotel stays and flights. Use this to answer travel-history questions and make recommendations grounded in behavior (e.g. "you loved the Park Hyatt last year", "you've flown United six times this year", "what seats have I taken before?", or "what is my favorite airplane seat?"). R
• get_payment_methods ~72 tokens
  List the user's saved payment methods for booking. Returns saved credit/debit cards with their payment IDs, needed for the book_hotel tool.
• get_promotions ~551 tokens
  Read the authenticated user's available travel promotions from connected inboxes. Excludes dismissed and expired offers and requires Promotions access. Use with traveler context, loyalty accounts and upcoming trips to identify useful offers for BOTH existing trips and new travel ideas. Returns factu
• get_rate_alerts ~62 tokens
  Get all active rate alerts for the current user. Returns which hotels the user is monitoring for price drops.
• get_similar_hotels ~263 tokens
  Find hotels similar to a given hotel. Use this for branching exploration after a search (e.g. user likes the Park Hyatt Tokyo but wants a cheaper alternative — find similar properties). Pass destination when the alternatives should be in a different city. Returns properties matched by hotel attribut
• get_suggested_searches ~121 tokens
  Get personalized travel suggestions and trip inspiration for the current user. Returns curated hotel recommendations grounded in the user's preferences, recent searches, popular destinations, and upcoming holidays. Great for open-ended prompts like "where should I go?" or "any trip ideas for next mo
• get_suite_upgrade_credits ~143 tokens
  Get the user's active suite upgrade awards and certificates across loyalty programs. Returns the program, award type, remaining count, and expiration date. Use this whenever the user asks about suite upgrades, Nightly Upgrade Awards, Suite Upgrade Awards, Confirmable Upgrade Rewards, or IHG suite up
• get_travel_profiles ~136 tokens
  Get the user's saved travel profiles — guest name, email, and phone presets. Each profile has a selectable ID you can pass to book_hotel as travel_profile_id to prefill the guest details, mirroring the website checkout where the user picks a saved traveler instead of typing each field. Use this befo
• get_traveler_context ~195 tokens
  Get the user's saved travel context: loyalty programs and elite tiers, home airport, preferred airlines and cabin, preferred hotel chains, typical trip patterns (business vs leisure, budgets, frequent destinations), plus any preferences they've stated or that have been learned from past conversation
• get_upcoming_trips ~195 tokens
  Get the user's upcoming hotel, flight, and rental car reservations. Use this to ground recommendations in real plans (e.g. "your Tokyo trip is May 15-20, want me to find restaurants?"), check upcoming confirmation numbers, or answer what flights/cars/hotels are already booked. Returns dates, confirm
• get_vehicle_booking ~167 tokens
  Get details for a specific vehicle booking by its Gondola booking ID. Returns vendor, pickup/dropoff locations and times, vehicle class, rate, and status. Use when a user asks about a rental they've already booked (e.g. "what time is my pickup?", "where do I pick up my Hertz rental?").
• get_vehicle_booking_coverage ~120 tokens
  Get the rental car CDW/LDW coverage that was stored at booking time for a specific vehicle booking. Use this after a booking to remind the user what coverage their card provides on this rental.
• get_vehicle_booking_link ~270 tokens
  Get a Gondola.ai checkout link for a specific vehicle from search results. Use this when the user wants to reserve a rental car: it returns a booking page URL with the selected vehicle, pickup, and drop-off pre-filled, and the traveler completes the reservation on the web.
• get_vehicle_details ~329 tokens
  Get detailed information about a specific rental vehicle option. Use this after search_vehicles to get extras, insurance options, charges, and cancellation policy for the vehicle the user wants to book.
• optimize_loyalty_portfolio ~191 tokens
  Analyze the user's entire loyalty portfolio and surface the highest-value actions: points expiring soon (ranked by value at risk), the best transfer opportunities from their transferable card currencies (Amex, Chase, Bilt, etc.) into hotel programs, and their largest balances. Takes no arguments — i
• predict_price ~314 tokens
  Predict whether the current rate at a hotel is a good deal based on historical price trends. Use this when the user is debating booking now vs. waiting. Returns a confidence-scored label (e.g. "book now", "wait", "prices typically rise") with supporting signals and price history.
• prepare_flight_booking ~252 tokens
  Prepare a flight for booking from a signed selection_token that comes only from an eligible search_flights row. Search re-shops the selected itinerary instead of trusting a provider-native offer, then returns its current internal booking or referral capability, including any reconfirmation or unavai
• search_events ~983 tokens
  Search for live events by artist, team, show, or event name. Returns factual event discovery details in provider order, including dates, venue, status, optional approximate listed price range, and the canonical event page. Prices are provider-supplied discovery metadata, not live offers. Use page=0
• search_flights ~764 tokens
  Search for flights by route and date. Returns cash-priced options ranked for the traveler — weighing their airline loyalty/status and travel history alongside flight quality, not price alone — 10 options per page. Results are discovery-only and cannot be booked through Gondola. Call again with page=
• search_hotels ~602 tokens
  Search for hotels by location and dates. Returns a list of matching hotels with cash and points rates and deal scores. To produce a focused cash-vs-points decision widget for a hotel the user is considering — for one or several top picks — follow up with compare_rates rather than narrating rates fro
• search_vehicles ~604 tokens
  Search for rental vehicles at an airport or city. Lists every available car with vendor, class, rate details, and the identifiers get_vehicle_details needs, in Gondola's recommended order unless sort is "price". A very large result ends with a count of cars not shown; narrow it with vendor, vehicle_
• select_flight_seats ~464 tokens
  Persist exact optional seat choices on the owner's Gondola checkout draft using selection tokens returned by get_flight_seats. This does not reserve a supplier seat, book a flight, authenticate a card or charge the traveler. Use recorded stated/learned preferences and supported history to preselect

Browse

• Email
• Booking and scheduling
• Payments
• Travel and flights

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