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

Annotations already cover the safe-read profile, and the description adds substantial extra context: the Fair Work Ombudsman 2026-2027 data source, explicit scope limitations, the underlying formula, and a documented REST fallback with a warning not to guess other paths.

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?

Front-loaded with usage guidance, which is good, but it then enumerates every output field even though an output schema exists, and layers in formula, REST fallback and docs links. The result is longer than needed and partly redundant with structured 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?

Given the tool's narrow purpose, the definition is complete: it covers scope, data source, formula, fallback, and where to find docs. With an output schema present and annotations covering safety, nothing an agent needs to call it correctly is missing.

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% and both parameters are documented in the schema, so the schema carries the load. The description adds marginal value by restating the inputs and providing a complete JSON argument example, slightly beyond the baseline for high coverage.

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: checking an Australian date against state/territory public-holiday tables, with the scope narrowed to state/territory-wide results. It distinguishes itself from regional/council/employer calendars, though it does not explicitly name the sibling tools (e.g., count_australia_working_days, check_global_working_day) it differs from.

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?

Explicit when-to-use ('when you need a state/territory-wide Australian public-holiday result') and when-not ('when you do not know the state/territory, or you need a local council, regional, employer, exchange or industry calendar'). It even instructs the agent to call directly rather than reason from memory.

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