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日本の給与・社会保険・労働法

by io.github.kishida-devil

定時決定(算定基礎届)をまとめて — 事業所全員分

decide_regular_remuneration_batch

Runs the annual 定時決定 for a whole payroll in one call, and reports which employees moved grade.

健康保険法第41条 puts every insured employee on the same schedule — the average of April, May and June pay, over the months with at least seventeen payment-basis days, applied from September to the following August. So June is the one month of the year when an office decides its entire payroll at once, and asking about one employee at a time is the wrong shape for the task.

Reach for this the moment more than a couple of employees are in play. Each row returns the same judgement as decide_regular_remuneration, plus whether that person changed grade, which is what decides how much filing there is. Pass previous_remuneration to get that comparison; without it the answer is null rather than false, because "no grade to compare" and "did not move" are different facts.

Pass acquired_on, left_on or revision_month and each row also says whether that employee is filed at all. 健康保険法第41条 leaves out anyone insured between 1 June and 1 July, anyone gone before the 1 July reference date, and anyone revised from July to September. The run totals to_file and not_required, which is the number of forms rather than the number of employees. A row that cannot be decided is returned in errors with its index and id, and the rest of the run still completes — do not discard a whole run over one bad row.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
defaultsNoApplied to any row that omits the field.
employeesYesOne entry per employee.

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so well: it discloses null-vs-false semantics for previous_remuneration, the meaning of to_file/not_required totals as forms rather than employees, partial-failure behavior (bad rows go to errors with index and id while the rest completes), and legal exclusions. The warning 'do not discard a whole run over one bad row' is exactly the kind of behavioral guidance an agent needs.

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

Conciseness5/5

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

The description is longer than average but every sentence earns its place: the legal context motivates the batch shape, the usage rule is front-loaded, and the remaining paragraphs cover optional-parameter effects, totals semantics, and error handling. The structure is logical and dense without redundancy.

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?

Given no output schema exists, the description covers the return contract well: per-row judgement inherited from decide_regular_remuneration, grade-change flag, filing totals, and errors with index/id. However, the exact per-row field names and the role of the defaults object in overriding employee rows are not explicitly addressed, leaving a minor gap for a tool of this complexity.

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

Parameters5/5

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

Schema coverage is 100%, giving a baseline of 3, but the description adds significant behavioral meaning beyond the schema: previous_remuneration's null-vs-false distinction, how acquired_on/left_on/revision_month influence whether an employee is filed, and what the aggregate totals mean. This substantially enriches the raw parameter definitions.

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

Purpose5/5

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

The first sentence states a specific verb and resource: 'Runs the annual 定時決定 for a whole payroll in one call, and reports which employees moved grade.' It clearly distinguishes the batch tool from the singular sibling decide_regular_remuneration by framing single-employee queries as 'the wrong shape for the task.'

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

Usage Guidelines5/5

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

Explicit when-to-use guidance is provided: 'Reach for this the moment more than a couple of employees are in play.' It also tells when each optional parameter matters (previous_remuneration for grade comparison; acquired_on, left_on, revision_month for filing status), and contrasts with the one-at-a-time alternative.

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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