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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds real context the annotations cannot: the source is a 30-year fleet table, and Southern Hemisphere rows carry their closed 2026 season result (summit, modelled, updated date) alongside the prediction, which tells the agent to expect mixed actual/predicted data.

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?

Three sentences, front-loaded with the purpose then the filters then the data caveat, with no filler. The final sentence on Southern Hemisphere rows is dense but earns its place by preventing a misinterpretation of the returned data.

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 an output schema present, the description needn't document return fields, and the annotations cover the safety profile; together with the filters and data-source note this is nearly complete for a filtered ranking tool. Only the absence of an explicit sibling comparison keeps it from a 5.

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 100%, so all three parameters (tier, limit, region) are already documented, including the default 10 / max 50 on limit. The description restates the region and tier vocabularies but does not add syntax beyond the schema and even omits the colloquial aliases (andes, alps, japan, rockies) that the schema lists. Baseline 3 is appropriate.

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 opens with a specific verb+resource pair ('Ranked El Niño ski resorts') and names its data source (SnowSure's 30-year fleet table), plus the two filter axes (region, tier). This makes it clearly distinguishable from the sibling signal tool get_elnino_signal and from get_season_leaderboard, which rank resorts on general rather than El Niño criteria.

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?

Three quoted example queries ('best El Niño resorts in Europe', 'where will it snow most in 2026-27', 'is El Niño good for the Alps') give the agent concrete triggering contexts, and the filter values are spelled out. It stops short of naming an alternative or an explicit when-not case (e.g., for the raw ENSO signal, use get_elnino_signal), so it falls short of a 5.

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