list_travel_guides_for_destinationList all published Tuki travel guides linked to a specific destination. Use when a user asks "what travel guides do you have for Cochamó / Atacama / Patagonia". Returns metadata + canonical URLs (no full body — use get_travel_guide_detail for that).
| Name | Required | Description | Default |
|---|---|---|---|
| destination_slug | Yes | Destination slug (e.g. "cochamo"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by specifying the output type ('metadata + canonical URLs') and explicitly excluding full body content. It also notes the 'published' filter, which is not declared in annotations. This is valuable for an AI agent deciding whether to use this tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action, and no wasted words. Every element adds value: what it does, when to use, what it returns, and what it doesn't.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one parameter and comprehensive annotations, the description fully covers usage context, return type, and exclusions. It also points to the relevant sibling for follow-up, making it complete for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides a description for destination_slug with an example. The tool description adds example destination names in the usage clause but does not provide additional parameter semantics beyond what the schema states, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') and resource ('published Tuki travel guides') with a clear scope ('linked to a specific destination'). It distinguishes from sibling tools like search_travel_guides and get_travel_guide_detail by explicitly noting this is for listing guides for a destination and not for full bodies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool: 'Use when a user asks "what travel guides do you have for..."' and provides an alternative ('get_travel_guide_detail') for when full body is needed. This is clear guidance with no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool targets a distinct resource or action: get_* tools retrieve details for specific entity types, search_* tools query specific verticals, and list_* tools enumerate categories. The potential overlap between search_accommodations and search_hotels is explicitly disambiguated in descriptions, and search_unified vs per-vertical searches have clear routing rules.
All tools follow a consistent verb_noun pattern: get_*_detail for lookups, search_* for queries, list_* for enumerations, and recommend as the sole one-word but still predictable exception. Naming is uniform with snake_case throughout, making the overall structure highly predictable.
With 23 tools, the server is on the heavier side, but the breadth of verticals (accommodations, cars, events, experiences, hotels, destinations, bus routes, travel guides, solutions) justifies each tool's presence. The number is slightly above the ideal but still well-scoped for a multi-category marketplace.
The tool surface covers the full browsing and recommendation lifecycle: search and list across verticals, retrieve detailed information for any entity, access editorial travel guides, and generate checkout URLs for booking. No critical operations are missing for the server's purpose as a read-only recommendation and conversion layer.