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

No annotations are provided, so the description carries the full behavioral burden. It helpfully notes that missing inputs fall back to documented defaults and lists the main output components, but it does not state that the calculation is non-mutating, describe rounding or currency assumptions, or explain the response format.

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 short and front-loads the core action. The semicolon-separated output list is efficient, though the repeated word in 'calculator calculator' is an avoidable blemish.

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 nine-parameter calculator with no annotations and no output schema, the description is only moderately complete. It names several outputs and mentions defaults, but it does not explain the calculation logic, currency assumption, or full response shape, leaving gaps for an agent trying to interpret results.

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%, so the input schema already documents all nine parameters and their defaults. The description mentions default fallback behavior but does not add parameter-specific meaning, format details, or interaction rules beyond what the schema provides.

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 states a specific verb and resource: run the New Zealand final pay and annual leave calculator. It also lists the main computed components, making the tool's domain clear. However, it does not differentiate from the related sibling calculator_describe, and the repeated word 'calculator calculator' slightly muddies the statement.

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 implies when the tool is relevant by naming the New Zealand final pay and annual leave scenario, but it gives no explicit when-to-use guidance, no exclusions, and no reference to the sibling calculator_describe or other alternatives. An agent must infer usage from the domain alone.

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

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

With no annotations and no output schema, the description carries the full burden of disclosing return content, and it does name the three categories returned (inputs, outputs, assumptions/tables), which is genuinely useful. It does not say whether the response is static documentation or computed from configuration, nor anything about size or format. Read-only nature is implied by the passive phrasing but never stated.

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 tightly-packed sentence with no filler; the return content is front-loaded in the order an agent would want it (inputs, then outputs, then assumptions). The one blemish is that it is a verbless noun phrase, reading more like a table of contents than a definition, which costs a little immediate clarity.

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 zero-parameter, no-output-schema documentation tool, the description covers the main thing an agent needs: what comes back. Given no annotations exist, it could go slightly further (e.g., stating that it is read-only, non-mutating, and safe to call before `calculate`), but nothing essential for correct invocation 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?

The tool takes zero parameters, so the baseline is 4 per the rubric. The description appropriately spends no space on parameter explanation and instead describes the parameter domain of the sibling calculator, which is a sensible use of the sentence.

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 clearly enumerates what the tool delivers: input specifications (units, ranges, defaults), output specifications, and underlying assumptions/tables. This is specific enough that an agent knows it is a documentation/metadata tool rather than a computation. However, it never states a verb (e.g., 'Returns...') and never names the sibling `calculate` as the counterpart, so the distinction is inferred rather than declared.

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?

There is no guidance on when to call this versus `calculate`, nor any workflow hint such as 'call this first to discover valid arguments for calculate'. The agent must infer the ordering itself. No exclusions or preconditions are stated.

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

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

With no annotations provided, the description carries the full burden and handles it well: it disclaims purchases, orders, payments, and guaranteed quotes, states it is free, and discloses what information is returned. This gives the agent confidence that invoking this tool is informational rather than side-effectful.

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 compact, front-loaded with 'Read first', and uses plain language to pack meaningful behavioral caveats into three sentences. It loses a point because the title repeats the 'not a purchase / not guaranteed' point in the description, creating slight redundancy.

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 parameterless documentation/description tool with no output schema, the description is complete: it names the action being described, explains the real-world consequences (or lack thereof), and summarizes the returned content. There is no missing critical information an agent would need to decide whether to call it.

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?

The tool has zero parameters, so the schema is trivially complete and the description does not need to explain parameter meanings. The baseline for 0-parameter tools is 4, and the description appropriately focuses on the tool's purpose rather than irrelevant parameter details.

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 clearly identifies the tool as explicating submit_enquiry on Final Pay Calculator NZ, with a specific verb ('states plainly'), a specific resource ('what submit_enquiry does'), and a scope that distinguishes it from actually submitting. The title reinforces the contrast with a purchase or guaranteed quote, so an agent can immediately tell this is a description tool, not the action tool.

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?

'Read first' is a clear temporal usage instruction, and the description explicitly names submit_enquiry as the action it explains, implying this should be consulted before invoking that tool. It does not explicitly compare against calculator_describe or enquiry_fields, but the intended context is clear enough.

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

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

With no annotations provided, the description carries the burden of explaining behavior. It discloses the kind of data returned (field metadata) and even notes the conditional nature of allowed options. It does not explicitly state that the operation is read-only or describe the response shape, but for a simple introspection tool the behavioral disclosure is solid.

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 a single well-structured sentence that front-loads the tool's purpose and then adds a practical usage pointer. No words are wasted.

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 has no parameters, no output schema, and no annotations, the description covers the essential information an agent needs: what the tool provides and how that output relates to the submission workflow. It could be slightly more explicit about the output format, but the description is adequate for correct selection and 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?

The tool takes zero parameters, so the baseline is 4. The description adds relevant context by explaining how the returned field keys should be used when calling submit_enquiry, which is more than the empty schema alone provides.

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 clearly identifies what the tool exposes: every field of the Final Pay Calculator NZ enquiry, including key, label, type, required status, help text, and allowed options. It is distinct enough from siblings like calculate and submit_enquiry, though it does not explicitly contrast itself with enquiry_describe or use a direct verb like 'returns'.

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 provides clear usage context by telling the agent to pass answers to submit_enquiry keyed by field key, which implies this tool is used to discover the available fields before submission. It does not explicitly state when not to use this tool or name alternatives, but the intended workflow is clear.

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

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

No annotations are provided, so the description carries the full burden and does so well: it discloses the two-call sequence, that step 1 only validates and returns a summary/token, that submission triggers a mandatory email click before any provider sees the data, that no more than three firms receive details, and that the person can stop further distribution.

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-loads the most decision-relevant fact ('NOT a purchase') and uses clear Step 1/Step 2 structure. It loses a little economy by repeating the full consent sentence already present in the schema, which is duplication rather than new 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?

With no output schema, the description compensates by describing what step 1 returns (summary, consent line, confirmation token) and what the person receives after step 2 (an email with a click-through link). For a nested-object, two-phase tool with no annotations, nothing essential for correct invocation 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 description coverage is 100% and the schema already documents all three parameters, including the consent text verbatim. The description adds workflow meaning for the token ('from step 1, after the person has approved the summary') and the field-key convention, but largely restates what the schema provides, so it sits at the baseline.

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 (submits) and resource (an enquiry to Final Pay Calculator NZ) and immediately disambiguates the intent with 'NOT a purchase, NOT a guaranteed quote.' That framing cleanly separates it from calculate/calculator_describe siblings, so an agent knows this is a routing/contact request rather than a computation.

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 an explicit two-step protocol with the exact condition for the second call ('only if the person agrees') and the gating prerequisite (confirmation token from step 1). It doesn't explicitly state when to prefer this over calculate or when to call enquiry_fields first, though the reference to 'field key from enquiry_fields' implies the dependency.

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