get_aurora_forecast

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

With no annotations, the description carries full burden. It explains the two modes (with/without coordinates) and output format (probability percent or peak). However, it does not mention limitations, model accuracy, or coordinate format (likely decimal degrees), leaving some behavioral gaps.

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 succinct sentences front-load the model and time range. Every sentence is purposeful, with no wasted words. Structure is ideal for quick agent comprehension.

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?

Given the presence of an output schema (not shown), the description does not need to detail return format. It covers the two parameter modes and output semantics. However, it could explicitly state coordinate format (decimal degrees) for completeness.

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%, so description must add meaning. It clearly explains that latitude and longitude are optional and affect output: if provided, returns probability at that location; if omitted, returns peak probability over the oval. This adds significant value beyond the schema.

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 clearly states it provides a short-term (~30-90 min) auroral-oval forecast using OVATION model. It distinguishes itself from sibling tools like get_kp_forecast by specifying the time range and output (probability at location or peak).

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?

The description implies usage for short-term aurora prediction ('will I see aurora tonight?') but does not explicitly contrast with siblings or state when not to use it. Usage guidance is implicit rather than explicit.

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