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

The description reveals that the verdict pulls in MET Norway cloud cover, ISS passes, aurora chance, and other external live data. The annotations declare openWorldHint=false, which contradicts that external data sourcing. Because the description contradicts an annotation, this scores 1.

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?

A single dense sentence front-loads the verdict and then lists components with semicolons. It is efficient but slightly run-on; every clause adds information.

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 complex aggregate nature and the presence of an output schema, the description covers all major data sources and the default location. It is complete enough for an agent to understand what the tool returns.

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 coverage is 100%, so the baseline is 3, but the description adds a meaningful default not in the schema: Warsaw when no place is given. The other parameters are fully documented in the schema, so no further compensation is needed.

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 names a specific resource — tonight's sky verdict — and enumerates the exact components returned: dark hours, cloud cover, moon, ISS, aurora, planets, and meteor shower. This distinguishes it from the individual sibling tools, which each cover only one of those components.

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?

It states the context — a Polish city, defaulting to Warsaw — but does not explicitly say when to choose this aggregate verdict over the individual sibling tools like aurora_forecast or iss_passes. The aggregate nature is implied rather than routed.

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