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日本の給与・社会保険・労働法

消費税率(日付指定・軽減税率・改定履歴)

consumption_tax

The consumption tax rate in force on a date, with the national and local parts, and the reduced 8% rate for food and newspapers. Pass amount to have the tax worked out.

Japan changed the rate four times since 1989 (3% → 5% → 8% → 10%), and the reduced rate has existed only since 2019-10-01. A back-dated invoice or a credit note against an old sale is charged at the rate of the original transaction, not today's, so the date matters more often than people expect. Set history to see every change with its statute.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoYYYY-MM-DD. The rate in force on that day. Defaults to today.
as_ofNoAlias for date. YYYY-MM-DD.
amountNoTax-exclusive amount in yen, to compute the tax.
historyNoReturn every rate change since 1989 instead of one date.
reducedNoTrue for the 8% reduced rate — food and drink excluding alcohol and eating out, and subscribed newspapers issued twice a week or more (平成28年法律第15号).

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
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 does so well. It discloses the rate-change history (3% → 5% → 8% → 10%), the reduced rate's start date of 2019-10-01, the back-dating rule for invoices and credit notes, and that history mode returns changes with statutes. These are meaningful behavioral traits beyond what annotations would likely convey.

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?

Three dense sentences with no filler. The core function is front-loaded, and the second and third sentences provide behavioral context that an agent actually needs rather than repeating schema content. Every sentence earns its place.

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 5-parameter tool with no annotations and no output schema, the description covers the key behaviors: date-specific rates, reduced-rate eligibility, optional tax computation, and history mode. Minor gaps remain around the exact output shape when `amount` is passed and what happens if both `date` and `as_of` are provided, but these are not critical for basic invocation.

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 because the schema already documents all five parameters in detail. The description adds useful semantic context for `date` (back-dating matters more than expected) and `history` (returns changes with statute), raising it above baseline. It does not add much for `as_of` or `amount`, which are already well covered by the schema.

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 identifies the resource as the Japanese consumption tax rate in force on a date, including national/local split and the reduced 8% rate. This distinguishes it from the payroll and insurance sibling tools. It lacks an explicit verb like 'returns', but the functional intent is unmistakable.

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 concrete usage context: back-dated invoices and credit notes against old sales should use the original transaction date, and it warns that date matters more often than people expect. It does not name alternatives or explicitly state when not to use the tool, so it falls short of perfect routing guidance.

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.