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

Annotations already declare read-only, non-destructive, closed-world behavior, so the bar is lower; the description still adds real value by framing the output as a forecast with a range rather than a guarantee. No output schema exists, and the forecast/no-guarantee caveat is the key behavioral nuance an agent needs.

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 zero filler; the usage trigger is front-loaded and the disclaiming clause follows immediately. Nothing could be removed without losing meaning.

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 correctly characterizes the return as a forecast plus range. Combined with a fully documented schema and safety annotations, an agent has enough to invoke it correctly, though a pointer to the actuals/live-waits alternatives would close the last gap.

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%, with date, days, and park_id all fully documented in the schema, so the baseline is 3. The description reinforces the 30-day horizon and single-park scope but adds no format, default, or edge-case detail beyond the schema.

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?

States a specific verb+resource (crowd forecast for one park) and scopes it to the next 30 park-local days. It implicitly separates itself from live/actual data ('not a guarantee or a live wait'), though it never names the siblings parky_get_crowd_actuals or parky_get_waits explicitly.

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?

Gives a clear trigger condition ('when a visitor asks how crowded one supported park may be') and an implicit exclusion via 'not a guarantee or a live wait'. It stops short of naming the alternative tool an agent should call for actuals or live waits.

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