get_ato_benchmarks

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does this well by disclosing the denominator rule ('without it every ratio is not_supplied'), the special zero-passing caveat for other_income, and the non-judgmental nature ('This is a comparison, not a finding that anything is wrong'). It does not mention rate limits or side effects, but for a comparison tool this is acceptable.

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?

The description is front-loaded with the core purpose and each sentence adds useful context. The final sentence 'Uses ato-benchmark-compare' is an implementation detail that adds little for an AI agent, but it is not harmful. Overall the description is dense and efficient without being bloated.

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 13 parameters and no annotations, the description covers the essential context: purpose, value formats, industry source, the other_income dependency, and the fact that this is a benchmark comparison rather than a finding. An output schema exists, so return-value details are not required. It could be slightly more explicit about year and other optional bucket semantics, but the current level is largely complete for correct invocation.

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 adds meaningful semantics: amounts are decimal strings, industry is an ATO business-type name, and other_income affects ratio calculation. It does not individually explain every bucket parameter, but 'bucket totals' plus the schema titles provides reasonable coverage for the remaining fields.

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 opens with a specific verb and resource: 'Compare bucket totals against ATO small-business benchmarks.' This clearly distinguishes the tool from siblings like list_ato_benchmark_industries, which lists industry names, and get_div7a_benchmark_rate, which deals with a different benchmark. The added note 'This is a comparison, not a finding' further sharpens the purpose.

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 clear context on when to use the tool and how to prepare inputs, such as referencing list_ato_benchmark_industries for valid industry names and explaining that other_income is required for any ratio. It does not explicitly state when not to use this tool versus alternatives, but no obvious alternative provides the same comparison functionality, so the guidance is sufficient.

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