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日本の給与・社会保険・労働法

by io.github.kishida-devil

随時改定(月額変更届)の要否判定

judge_monthly_revision

Decides whether a pay change forces the standard remuneration to be revised, and answers SEPARATELY for health insurance and pension — the tables differ, so a change routinely moves one and not the other. Above roughly 665,000 yen the pension table is exhausted, so a large raise for a well-paid employee moves several health grades and no pension grade.

Do not try to reason this out unaided. Neither the "two grades or more" test nor the requirement that FIXED pay changed appears anywhere in 健康保険法 or its regulations — both come from 昭和36年 保発第4号, a ministerial notice, along with four exceptions near the top and bottom of each table where a single grade is enough. Overtime alone never triggers a revision, and a rise in fixed pay whose three-month average comes out LOWER is excluded even at a wide grade gap.

When the answer is no, the response says which requirement failed rather than just false. If the three-month average is distorted by a seasonal peak, see judge_annual_average.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthsYesThree months as "remuneration:payment_basis_days", comma separated — e.g. "350000:31,352000:30,349000:31". 支払基礎日数 is calendar days for monthly-paid staff, or days actually worked for daily-paid staff.
worker_typeNogeneral = 一般の被保険者 (17-day threshold). part_time_short_hours = 短時間就労者, works shorter hours but meets the three-quarters test (17 days, with a 15-day fallback that exists ONLY in 定時決定). short_time_insured = 短時間労働者 at a 特定適用事業所 (11 days). Defaults to general.
fixed_pay_changeYesWhether FIXED pay changed: base pay, rate changes, a new or altered fixed allowance, a change of pay basis. Overtime and other variable pay are not fixed pay — use "none" and the tool will explain why no revision follows.
current_remunerationYesThe 報酬月額 the CURRENT grade was based on — the actual pay figure, not the 標準報酬月額. The upper and lower exceptions turn on actual pay, so substituting the grade's standard value gives a wrong answer at the extremes.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it does so exceptionally well: it reveals the separate health/pension answers, the ~665,000 yen pension ceiling, the ministerial-notice legal source, the overtime exclusion, and the failed-requirement explanatory output. This goes far beyond the structured fields and gives an agent real insight into tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core three paragraphs are front-loaded and information-dense, but the sentence 'When the answer is no, the response says which requirement failed rather than just false' is repeated verbatim dozens of times. This severe redundancy obscures the otherwise good structure and wastes significant token budget.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a no-output-schema decision tool, the description covers the legal basis, key exceptions, scope, and failure-reporting behavior, while the input schema supplies parameter details. The main gap is an exact description of the output shape for a 'yes' or mixed health/pension answer, but the domain behavior is otherwise sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents the months format, worker_type thresholds, fixed_pay_change meaning, and the current_remuneration actual-pay caveat. The description adds useful domain context but no new input-format or parameter-level meaning, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence clearly names the exact decision ('whether a pay change forces the standard remuneration to be revised'), the resource (standard remuneration), and the separate health/pension scope. It does not explicitly name sibling tools or state what it is not, so it falls short of the strongest differentiation, but the function is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the trigger ('a pay change') and the decision context (monthly revision), and it warns against attempting the reasoning unaided. However, it never says when to prefer judge_annual_average, decide_regular_remuneration, or another sibling, and it gives no explicit exclusions or alternatives.

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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TDQS

A3.8/5.0
Disambiguation4/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness4/5

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.

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