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Gondola Award Travel Search

by ai.gondola

update_traveler_profile

Save a learned travel preference or experience to the user's traveler profile. Use when the user shares preferences, likes, dislikes, or trip experiences that should inform future recommendations. Examples: 'Prefers boutique hotels over chains', 'Always takes a window seat', 'Vegetarian — recommend plant-based restaurants', 'Hated ground floor rooms'. Don't save temporary logistics like 'my flight lands at 3pm'. Entries are appended, so saving one preference never overwrites another.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profile_entryYesThe preference or experience to save. Be specific and actionable. Good: 'Prefers ocean-view rooms'. Bad: 'Liked the hotel'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior4/5

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

Discloses a key behavioral trait beyond annotations: entries are appended, so one save never overwrites another. This complements the destructiveHint:false annotation by explaining why the operation is non-destructive. It also implies the profile accumulates entries over time.

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 compact and well-ordered: action, when-to-use, examples, exclusions, and behavioral note. Every sentence contributes and the most important context appears early.

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 single-parameter tool with a thorough schema, annotations, and an output schema, the description covers usage, exclusions, and side effects. An agent has enough guidance to call this tool correctly and decide when not to call it.

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?

The input schema already describes profile_entry well with good and bad examples, so the description need not repeat much. It adds value by clarifying the semantic scope (preferences, likes, dislikes, experiences) and explicitly excluding temporary logistics, which enriches the single paramter's meaning.

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?

States a clear action ('Save'), a specific resource ('traveler profile'), and the exact kind of content to save: learned preferences and experiences. The examples further disambiguate the tool from sibling read-only tools like get_traveler_context and get_travel_profiles.

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?

Explicitly describes when to use the tool ('when the user shares preferences, likes, dislikes...') and what not to save ('temporary logistics'). It doesn't name sibling alternatives, but the when/when-not guidance is strong enough to prevent misuse in most cases.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct resource and action; no two tools have overlapping purposes. For example, search_hotels, get_hotel_details, get_hotel_reviews, and get_hotel_stats all address different aspects of hotel research.

Naming Consistency5/5

Tool names follow consistent patterns: search_ for searches, get_ for retrievals, book_ for bookings, and a few standalone verbs like cancel_, create_, delete_. All use snake_case with no mixing of conventions.

Tool Count4/5

With 31 tools, the server is on the high side but covers a broad domain (hotels, flights, vehicles, loyalty, payments). Most tools are justified, though a few hotel analysis tools could potentially be consolidated.

Completeness3/5

Hotels and vehicles have near-complete lifecycle coverage (search, details, book, manage), but flights are missing a book_flight tool, and hotel cancellation is not present. These gaps limit completeness.

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