get_aurora_forecast

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

No annotations are provided, so the description carries the full disclosure burden. It does state the return shape ('current Kp index, visibility probability, and 3-day forecast'), which is useful, but it says nothing about behavior for unknown locations, whether the location name must be pre-resolved through search_location, or what the visibility probability actually means.

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?

The description is a tight docstring: purpose first, then Args, then Returns, with no filler or repetition. The standard formatting adds a little white-space padding, but every content line earns its place.

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?

Adequate for a single-parameter forecast tool, especially since an output schema exists to document return values. However, it omits the relationship with search_location (should the agent validate the name first?), failure behavior for unrecognized locations, and any differentiation from the weather-forecast siblings — gaps that matter given the sibling set.

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?

Schema description coverage is 0%, and the description compensates with 'Name of the city/location (e.g. "Tromso", "Prague")', adding a concrete semantic and realistic examples the bare string schema lacks. It stops short of clarifying whether non-city locations or unlisted names are accepted, so the compensation is good but not complete.

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 opening sentence, 'Get Aurora Borealis (Northern Lights) forecast and visibility probability,' names a specific verb, a distinct resource, and the output the caller gets. The resource ('Aurora Borealis') clearly separates it from the weather, location, and theme siblings, so an agent can pick it without inspecting the schema.

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?

There is no guidance on when to choose this tool over get_current_weather or get_weather_forecast, and no prerequisites are stated — such as resolving the location via search_location before calling. The only usage signal is implied by the tool name and purpose line, which does not meet the bar for explicit routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.