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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: the data is measured (not forecast), spans ten winters, and covers specific metrics and elevation bands. However, it does not reveal details like units, whether values are averages, or how month=0 behaves, which the agent would need for interpreting results.

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 sentences with no filler. The first sentence front-loads the core value proposition and data scope; the second cleanly specifies the optional filter. Every phrase contributes, and the structure is immediately scannable by an agent.

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 two-parameter tool with no output schema, the description adequately specifies the inputs (resort, optional month) and the nature of the results (typical snow, snow days, day/night temps, elevation bands). It stops short of giving exact response formatting or units, but the invocation requirements are clear enough for a correct call.

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 the description must compensate. It does clarify that month is an optional 1-12 filter, which adds meaning beyond the bare integer type. However, it does not explain the default value 0 or explicitly define that resort must identify a single resort, leaving some semantics to the schema's field names alone.

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 tool's purpose: providing ten winters of measured historical snow and temperature data for a specific resort, broken down by month and elevation band. It is distinct from siblings like snow_forecast or daily_snow, though it does not explicitly name them. The use-case phrase 'is February reliable here' reinforces the historical, climatological scope.

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 gives a clear context for when this tool is appropriate: answering questions about historical reliability and typical conditions for a resort. It does not name alternatives or state when not to use it, but the 'history' framing and the explicit optional month filter imply it is for seasonal/climatological queries rather than current conditions or forecasts.

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