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Live snow & forecast

get_resort
Read-onlyIdempotent

Get snow conditions, resort guide, photo gallery, or detailed text for a ski resort by specifying a slug and card type. Live data for AI agents.

Instructions

Single-resort data with a REQUIRED card parameter that picks the interactive UI. card=guide → resort info card (elevation, lifts, season dates). card=photos → photo gallery carousel. card=snow → snow conditions card (score, base depth, forecast). card=full → detailed markdown only, no card. "Resort guide" → card=guide. "Photos/gallery" → card=photos. "Conditions/forecast" / "is it open right now, base depth, lifts open of total" → card=snow (open status, base depth, and lifts open of total). Prefer get_resort_info / get_resort_photos when available (same cards).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardYesUI card type: guide (resort info), photos (gallery carousel), snow (conditions), full (text only)
slugYesResort slug identifier (e.g., "aspen-mountain", "niseko-hanazono-resort", "jackson-hole")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resortNoStructured resort payload for inline widget rendering.
markdownYesHuman-readable markdown summary (required for ChatGPT Instant mode).
Behavior4/5

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

Annotations already indicate read-only and non-destructive behavior. The description adds context about interactive UI and card selection, but the safety profile is already clear from 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?

The description is well-structured with clear sections and examples. While slightly long, every sentence adds value and the front-loading is effective.

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?

Given the low complexity (2 required params) and presence of output schema and sibling list, the description covers purpose, usage, parameter details, and alternatives comprehensively.

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%, but the description adds significant meaning by explaining each enum value with natural language examples and showing how to use the card parameter. This aids agent understanding beyond the schema.

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 the tool provides single-resort data with a required card parameter for interactive UI. It distinguishes itself from sibling tools by mentioning get_resort_info and get_resort_photos as alternatives.

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?

The description explicitly lists when to use each card value with real-world queries, and advises preferring other tools when available. It provides concrete examples for each card type.

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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