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OlaChill Japan Travel

Estimate a charter coach price

get_charter_quote
Read-onlyIdempotent

Use this when the user wants a price for a specific charter trip with a bus, coach, minibus or van and driver: pickup, destination, date(s) and group size (for example '45-seat bus Tokyo to Hakone on 20 March for 40 people', a school trip, a company outing or a multi-day group itinerary). This is step 2 of the charter workflow, after or instead of search_charter_vehicles. Japan: returns the fixed 10-hour package price per vehicle per day, the number of vehicles and days, the overtime rate, and what is included or extra (tolls, parking, driver lodging). Europe (Schengen countries): returns an estimated price range from OlaChill's rate table. Every result is an estimate that OlaChill confirms after checking vehicle availability. Read-only: nothing is sent, saved or reserved. Do not use it to submit the request (use request_charter_quote once the user agrees), for bus or train tickets, or for 1–6 people on an airport run (use search_private_transfers).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesTravel date as YYYY-MM-DD (Japan time for Japan trips).
localeNoLanguage for product names and page links (en, ja, ko, zh = Simplified Chinese, tw = Traditional Chinese, es, fr, de, it, th, id, vi). Default en.
pickupYesPickup city, airport, station or address area, e.g. 'Narita Airport', 'Shinjuku', 'Paris CDG'.
regionYesjapan, or europe for Schengen countries.
countryNoRequired when region is europe.
end_dateNoLast day for multi-day hire (omit for a single day) as YYYY-MM-DD (Japan time for Japan trips).
trip_typeNoDefault one-way for one date, multi-day when end_date is after date.
large_bagsNoNumber of large suitcases.
passengersYesNumber of travellers.
destinationNoMain destination or 'round trip' area, e.g. 'Mt Fuji', 'Kyoto', 'Versailles'.
pickup_hourNoPickup hour 0–23 local time (used for night surcharges in Europe).
vehicle_typeNoOptional vehicle_type from search_charter_vehicles. Omit to use the recommended size.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
hintNoWhat to try next.
errorNoPresent only when the call failed; a short reason the user can act on.
routeNo
regionYes
countryNo
currencyYes
overtimeNo
page_urlNo
validityNo
exclusionsNo
inclusionsNo
next_actionYes
vehicle_typeYes
vehicle_countNo
pricing_statusYes
estimated_priceNoTotal for all vehicles and days. high is null when the price is a single package amount before extras.
passenger_capacityNo
earliest_bookable_dateNo
supplier_confirmation_requiredYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only declare readOnly/idempotent/non-destructive; the description goes further by disclosing that every result is an estimate OlaChill confirms after checking vehicle availability, that Japan returns a fixed 10-hour package price with vehicle/day counts, overtime rate and inclusion/exclusion breakdown, and that Europe/Schengen returns a rate-table price range. That is substantive behavioral context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the trigger sentence and organized as trigger → workflow position → region-dependent output → caveat → exclusions, with no filler. The parenthetical example list is dense and slightly run-on, which mildly hurts scanability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 12-parameter, dual-region pricing tool it covers everything an agent needs: required trip facts, workflow ordering, region-conditional behavior and output shape, the estimate caveat, and negative routing to three sibling tools. With an output schema present, it correctly avoids re-specifying the return payload.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds usable semantics: a worked example ('45-seat bus Tokyo to Hakone on 20 March for 40 people'), the grouping of pickup/destination/dates/group size as the core inputs, and the region split that governs how country vs. region must be supplied. It stops short of explaining optional params like large_bags, pickup_hour or trip_type defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb+resource (estimate a charter coach price) and immediately bounds it by vehicle classes (bus, coach, minibus, van with driver) and required trip facts. It explicitly distinguishes itself from search_charter_vehicles (which it follows), request_charter_quote (the submission step), and search_private_transfers (1–6 airport runs), so an agent can route correctly without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use (user wants a price for a specific charter trip), positions it as 'step 2 of the charter workflow, after or instead of search_charter_vehicles', and states three when-not cases with the alternative tool named for each. Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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