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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world), and the description adds that results are per-park and retrospective. It does not describe the return shape, the crowd scale, pagination, or how forecast-vs-actual comparison is surfaced, which matters because there is no output schema.

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?

One sentence, front-loaded with the trigger condition and zero filler. Both clauses (actual crowds, forecast accuracy) add distinct 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?

For a two-parameter read tool this is nearly adequate, but the second advertised capability (forecast accuracy) is unexplained: with no output schema, an agent cannot tell whether the response includes prediction deltas, a crowd index, or raw values.

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 schema already documents park_id formats, the shorthand examples, and the 1-60 day window. The description only hints at the same concepts ('one supported park', 'recent days') without adding format or default detail, so the baseline of 3 applies.

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 the specific resource (observed crowds at a single supported park over recent days) and a second capability (forecast-accuracy comparison). It distinguishes itself from the forecast sibling parky_get_crowd_outlook through the phrase 'actually was', though it neither states a verb directly nor names the sibling.

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 a concrete trigger ('when a visitor asks how crowded one supported park actually was on recent days') plus a secondary use case (checking forecast accuracy). There are no explicit exclusions or named alternatives, so an agent must infer when the forecast tool is preferable.

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