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日本の給与・社会保険・労働法

by io.github.kishida-devil

労災保険率 — 事業の種類別

list_workers_compensation_rates

Workers compensation (労災保険) rates by business type, and the employer premium on a given 賃金総額.

The whole premium falls on the employer — nothing is deducted from the employee, unlike every other statutory premium. Rates run from 2.5/1000 to 88/1000 depending on the industry, a 35-fold spread, so this cannot be estimated. Pass the 事業の種類の番号 from the 労働保険関係成立届; omit it to get the whole table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoISO date the wages relate to.
wage_totalNo賃金総額 for the period, in yen — the same wage base employment insurance uses, so a commuting allowance counts and a reimbursement does not.
business_typeNo事業の種類の番号 (02-99), e.g. "35" for 建築事業 or "98" for wholesale and retail.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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 discloses that the entire premium falls on the employer, that nothing is deducted from the employee, that rates vary widely, and how the optional parameter changes output. It does not specify output shape or the effect of as_of in detail, but this is still substantial disclosure.

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?

Two tightly focused paragraphs with the main purpose front-loaded. Every sentence adds useful context: the premium, the rate range, why estimation is unsafe, and how to control the output.

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 3-parameter tool with no output schema and no annotations, the description covers the core invocation logic and key behavioral context well. It is slightly incomplete on as_of semantics and exact return format, but the schema plus the table/premium language close most gaps.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds real meaning by tying business_type to the 労働保険関係成立届 and explaining the omit-for-full-table behavior. It also clarifies that wage_total is the base for the employer premium, though as_of is left to the schema.

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 description clearly states that the tool lists 労災保険 rates by business type and computes the employer premium on a given 賃金総額. It is specific about the resource and behavior, and the scope is distinct from sibling tools like get_insurance_rates or national_insurance.

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

Usage Guidelines4/5

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

It explicitly says to pass the 事業の種類の番号 from the 労働保険関係成立届 and that omitting it returns the whole table. The phrase 'this cannot be estimated' gives a clear reason to use the tool for exact rates. It does not name alternative tools or exclusion conditions, but the context is strong.

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.

Resources