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日本の給与・社会保険・労働法

by io.github.kishida-devil

データ鮮度 — 各データの対象期間と次回改定

check_data_freshness

Japanese statutory figures change on fixed dates — insurance rates each March, employment insurance each April, minimum wage each October — and a stale table produces numbers that look plausible and are wrong. This reports what every dataset currently covers and when its next revision is due.

Worth calling before relying on a figure for a filing, and whenever a result is being checked against a date near one of those boundaries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool reports coverage periods and revision due dates and warns why stale data is dangerous, but it does not state whether the operation is read-only, describe response shape, or mention any side effects—though 'reports' implies a read operation.

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 three well-structured sentences: rationale, core capability, and use triggers. The risk framing is slightly verbose but earns its place by telling the agent why freshness matters, and the key capability is clearly stated.

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 zero-parameter reporting tool with no output schema, the description is complete: it states what the tool returns (current coverage and next revision date) and when to call it. An agent has everything needed to invoke it correctly.

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?

The tool has zero parameters and the schema has 100% description coverage, so there are no parameter semantics to add. The baseline 4 applies because no parameter documentation is needed.

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 states an exact action and object: it reports what every dataset currently covers and when the next revision is due. The opening also grounds the tool in the fixed-date revision calendar, making it distinct from sibling calculation/rule tools such as get_insurance_rates or get_minimum_wage.

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?

The description gives explicit triggers: call before relying on a figure for a filing, and whenever a result is checked near one of the revision boundaries. It does not list exclusions or alternatives, but the use cases are concrete enough for an agent to choose this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All 日本の給与・社会保険・労働法 toolsInspect
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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