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

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so the safety profile is covered. The description adds value beyond that by disclosing the cost profile ('read from the registry, no network call') and by explaining what `near_domain_edge` and the regional/global parent masking actually mean, which is real behavioral context.

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?

Every sentence carries information — masked parent models, convection semantics, edge-relaxation warning, and zero cost — and the cost fact is sensibly front-loaded as a closing note. It is slightly prose-heavy and the parenthetical model list is longer than strictly necessary, but nothing is filler.

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?

With no output schema, the description does the work of naming the meaningful return fields (`resolves_convection`, `near_domain_edge`) and explaining how to interpret them, which is what an agent needs to use the result. The remaining gap is input formatting rather than output meaning.

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 0%, so the description must carry the burden. It conveys that the two coordinates define 'a point' and that position determines domain-edge exposure, but never states coordinate format, datum, or accepted ranges, leaving genuine ambiguity for the only two required inputs.

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 identifies the resource (weather models) and the scope (those covering a given point), and the rhetorical framing 'Which weather models actually cover a point' resolves to a list operation. It does not name a sibling because none of the listed siblings compete for this job, so explicit differentiation isn't needed.

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 gives one concrete usage cue — cite `resolves_convection` models 'when asked where a storm will actually break' — which implies the decision context. But there is no statement of when to call this versus alternatives such as `get_forecast` or `get_storm_risk`, so routing guidance is only partial.

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