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

Annotations already establish the safe, read-only, closed-world profile, so the bar is lower. The description restates the computed metric rather than adding operational behavior such as data freshness, update cadence, or whether odds are historical vs. predicted; nothing is contradicted, but little is added beyond the annotations.

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?

A single front-loaded sentence defining the metric and its components, with zero filler. It could be marginally tighter, but every clause maps to real content an agent needs.

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 saying what comes back: drop frequency, catch probability, and timing recommendations. Combined with the sparse three-parameter schema, an agent has enough to call it correctly, though the filtering effect of date/park on the returned odds is unstated.

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?

Two of three parameters (date, park) carry schema descriptions and limit has explicit default/min/max constraints, giving roughly 67% coverage. The description adds no parameter-level meaning (e.g., how date interacts with the odds window, or that limit caps returned restaurants), so the baseline 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?

States a specific resource (WDW dining reservation drop odds) and enumerates the three outputs it produces: reappearance frequency, catch probability, and best day/hour. It is clearly distinct from a plain listing tool, though it does not explicitly contrast itself with the nearby get_hardest_dining_reservations 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?

"For hard-to-book Walt Disney World restaurants" gives a clear usage context that tells the agent this is for high-demand venues rather than all dining. It stops short of any exclusion or named alternative (e.g., use get_hardest_dining_reservations to rank candidates first), so it is context without routing.

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