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get_vehicle_booking_link

Read-only

Retrieve a booking link or in-conversation instructions for a specific vehicle based on your authentication status, using search results and trip details.

Instructions

Get a Gondola.ai deep link for a specific vehicle from search results.

Authenticated users get instructions to book in-conversation with book_vehicle plus the
web link as a fallback; anonymous users get the web link directly.

Args:
    search_id: Search ID from search_vehicles.
    vendor_code: Vendor code from search results.
    rate_code: Rate code of the selected vehicle.
    pickup_datetime: Pickup date and time in ISO format.
    dropoff_datetime: Drop-off date and time in ISO format.

Returns:
    Booking instructions tailored to the user's auth status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rate_codeYes
search_idYes
vendor_codeYes
pickup_datetimeYes
dropoff_datetimeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations (readOnlyHint=true) already indicate safety. Description adds behavioral context: authenticated vs anonymous users get different reference, and it returns instructions plus link. No contradiction. Lacks details on expiration or errors, but adequate.

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

Conciseness5/5

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

The description is concise: a two-sentence overview, then structured Args and Returns. No extraneous information, front-loaded with the key purpose.

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

Completeness4/5

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

With an output schema present, the Returns section is sufficient. The description covers key aspects: source of parameters, auth behavior, and fallback. Could mention error handling, but overall complete for a 5-param tool.

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

Parameters5/5

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

Input schema has 0% description coverage, but the description's Args section explains each parameter's origin (e.g., search_id from search_vehicles, vendor_code from search results). This fully compensates for the schema gap, making parameter semantics clear.

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?

The description clearly states it gets a Gondola.ai deep link for a specific vehicle from search results, distinguishing it from siblings like get_booking_link and book_vehicle. It specifies the auth-based behavior, making the purpose precise.

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

Usage Guidelines4/5

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

The description indicates it should be used after search_vehicles by referencing search_id and other fields from search results. It mentions auth status but does not explicitly exclude when not to use it, e.g., for actual booking. Still, clear context is provided.

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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