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日本の給与・社会保険・労働法

by io.github.kishida-devil

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get_statute_text

Returns the full text of a Japanese statutory provision, as published by e-Gov.

The judgement tools name the statute or notice their answer rests on, but not its words. Use this to quote the provision itself — a citation the reader can check beats a citation they have to take on trust, and Japanese payroll advice is routinely wrong in ways that only reading the article reveals.

Only the provisions this API cites are bundled (about 28 across 8 laws); call it with no ref to list them. Abbreviations as practitioners write them (健保法43条, 厚年法81条の2, 徴収法11条), a missing 第, and paragraph-level references all resolve to the article. For anything outside this set, say so rather than reciting it from memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoA citation such as "健康保険法第43条". Omit to list every provision available.

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?

No annotations are supplied, so the description carries the full burden. It discloses the limited bundled set ('about 28 across 8 laws'), the behavior of calling without a ref, resolution of abbreviations and missing 第, paragraph-level references resolving to the article, and the expected behavior for references outside the set. This is strong behavioral disclosure.

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

Conciseness4/5

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

The description is front-loaded with the core action and stays relevant throughout. The motivational sentence about checked citations is useful context but slightly expands length; overall every major point earns its place without being bloated.

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

Completeness5/5

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

For a one-parameter, no-output-schema tool with no annotations, the description is operationally complete. It covers what the tool returns, how to enumerate available provisions, how references are normalized, and how to handle unsupported citations. An agent can invoke this tool correctly without further inference.

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?

Although the schema already describes ref, the description adds significant meaning: it documents omission semantics, gives practical abbreviation examples like 健保法43条 and 厚年法81条の2, clarifies that a missing 第 is tolerated, and states that paragraph-level references resolve to the article. This goes well beyond the schema's minimal description.

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 names a specific verb and resource: 'Returns the full text of a Japanese statutory provision, as published by e-Gov.' It also distinguishes itself from the judgement tools, which name a statute but do not provide its words, making it clear this tool is for retrieving quotable statutory text.

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?

It explicitly says when to use the tool: 'Use this to quote the provision itself.' It also sets boundaries by noting the bundled corpus is limited, telling the agent to 'call it with no ref to list them,' and instructing that anything outside the set should be acknowledged rather than invented.

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