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tool_find_aurora_destinations

Rank budget-friendly northern lights destinations using NOAA KP-index, aurora-zone airports, and flight prices within 60 days.

Instructions

KILLER: Reverse search - "where to see northern lights cheapest, next 60 days?"

Combines NOAA KP-index forecast + aurora-zone airports + flight prices. Returns ranked destinations with aurora visibility scores.

Args: origin: Departure IATA code max_budget: Max flight budget days_ahead_min: Earliest departure (days from now) days_ahead_max: Latest departure currency: USD, EUR, etc. max_results: max destinations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originYes
max_budgetNo
days_ahead_minNo
days_ahead_maxNo
currencyNoUSD
max_resultsNo
Behavior3/5

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

The description discloses that it combines three data sources and returns ranked destinations with visibility scores. With no annotations, the description carries the burden but does not mention data freshness, limitations, or side effects. It is adequate but not comprehensive.

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 very concise, with a front-loaded 'KILLER' use case and a clear list of arguments. Every sentence serves a purpose without unnecessary details.

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 no output schema, the description explains that the tool returns ranked destinations with aurora visibility scores, which is sufficient for an agent to understand the return value. The tool is relatively simple, and the description covers its purpose, inputs, and output.

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 0%, so the description compensates by explaining all parameters (origin, max_budget, days_ahead_min, etc.) in context. Although the parameter names are self-explanatory, the descriptions add value by specifying units and constraints (e.g., 'days from now').

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 that the tool finds destinations for viewing northern lights by combining NOAA KP-index forecast, aurora-zone airports, and flight prices. This specific verb+resource distinguishes it from sibling tools like tool_find_destinations_by_budget or tool_score_destinations.

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 provides an example query ('where to see northern lights cheapest, next 60 days?') that implies when to use. It is clear this tool is for aurora-hunting, but it does not explicitly state when not to use or name alternatives.

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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