lookup_standard_remunerationMaps a monthly amount to its health grade (1-50) and pension grade (1-32), with the standard remuneration each resolves to and whether the pension grade was clamped. Use it to check a grade, not to compute premiums — calculate_payslip does that.
| Name | Required | Description | Default |
|---|---|---|---|
| remuneration | No | Monthly remuneration in yen. Omit to get the whole grade table instead of one lookup. |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description itself must carry behavioral disclosure. It states the output semantics, including the clamped-pension-grade flag, and clarifies the tool's scope. It does not explicitly state read-only or error behavior, but 'lookup' and 'check a grade' strongly imply non-mutating behavior.
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?
Two focused sentences with no filler. The core mapping behavior is front-loaded, and the usage distinction is placed right after, making the tool easy to scan and understand quickly.
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 covers the main return contents: health grade, pension grade, resolved standard remuneration, and clamp flag. It lacks exact response field names and out-of-range handling, but for a single optional parameter, this is nearly complete.
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 one parameter is already fully described ('Monthly remuneration in yen. Omit to get the whole grade table instead of one lookup.'). The description adds no additional parameter-level meaning 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?
Description names a specific verb ('Maps') and resource ('monthly amount' to 'health grade (1-50) and pension grade (1-32)'), and lists the returned elements. It also distinguishes itself from calculate_payslip by stating what it is not for.
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?
Explicitly instructs when to use it ('to check a grade') and when not to ('not to compute premiums'), naming calculate_payslip as the alternative. The schema adds another usage mode: omit the parameter to get the whole grade table.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have distinct, well-defined jobs—payroll calculations, revision judgments, eligibility checks, and data lookups are clearly separated. The main remaining risk is among the standard-remuneration judgment tools and the batch/singleton pairs, but their descriptions are detailed enough to prevent serious misselection.
There is a reasonably recognizable pattern with calculate_, judge_, check_, get_, and validate_ groups. However, several noun-phrase names (business_days, consumption_tax, national_insurance, commuting_allowance_exemption) and overlapping verb choices (decide_ vs judge_ vs check_) prevent a fully consistent verb_noun convention.
At 30 tools, the surface is materially larger than the 3–15 range that keeps an agent's selection burden low. Batch variants and three separate standard-remuneration revision tools add complexity, and although the domain is broad, the count feels heavy for an assistant to choose from confidently.
The core workflows are well covered: monthly pay, bonuses, overtime, standard-remuneration determination, year-end adjustment, resident tax, leave, worker classification, and invoice validation. Gaps exist around statutory benefit payments such as sickness or maternity allowances and retirement-specific processing, but most payroll and social insurance tasks can be completed without dead ends.