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

Annotations already declare readOnlyHint=true and destructiveHint=false. Beyond the annotations, the description adds behavioral context by detailing the return structure (kp_forecast array with time/kp/flag fields), the firmness labels (observed|estimated|predicted), and an explicit instruction to preserve those labels when presenting. It stops short of covering update cadence or edge cases like daytime/polar-night behavior, but the description clearly earns its space.

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 well-structured and front-loaded: a strong one-liner purpose statement, followed by the use-case guidance, then focused Args and Returns sections. Every sentence adds value—especially the note about keeping firmness labels, which is actionable. No fluff or redundancy.

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 2-parameter tool with no output schema, the description does a good job of documenting the return contract ({kp_forecast:[...], visibility assessment fields, attribution}) and the forecast's time dimension ('and when'). A few gaps remain: the forecast lookahead window (how many hours/days ahead) and a specific definition of 'visibility assessment fields' could be clarified. But overall, the description is sufficiently complete for this tool's complexity.

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 input schema provides no descriptions (0% coverage), so the burden is on the description. The description compensates by specifying decimal degrees format and valid ranges for lat (-90..90) and lon (-180..180). This adds genuine semantic value beyond merely restating titles like 'Lat' and 'Lon'. Ranges and format are fairly standard for geospatial parameters, but they are explicitly documented and accurate.

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 line, 'Get the aurora (n/s lights) outlook for a location,' is a specific verb+resource+context statement that also clarifies the domain. The description further differentiates from siblings by explaining the mechanistic combination of the Kp-index forecast with geomagnetic latitude, which is distinct from sibling tools like get_stargazing_conditions or get_light_pollution.

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 provides explicit usage guidance via the quoted use case, "Use for 'any chance of aurora tonight near X'", which clear signals intent and context of invocation. However, it doesn't name or exclude alternatives (e.g., when to prefer get_stargazing_conditions instead), leaving the when-not-to-use guidance 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.