Skip to main content
Glama

日本の給与・社会保険・労働法

年齢到達日(40/65/70/75)と保険料の変化

get_age_milestones

Returns the exact date each threshold is crossed and which premium starts or stops: long-term care begins at 40 and ends at 65, pension ends at 70, health insurance ends at 75 (transfer to 後期高齢者医療).

Compute this here rather than by subtracting years. Under 年齢計算ニ関スル法律 an age is reached the day BEFORE the birthday, so someone born on the 1st of a month attains it in the previous month and their premium changes a month earlier than expected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNoDate to judge against. Defaults to today.
birth_dateYesDate of birth, YYYY-MM-DD.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively does so by revealing the non-obvious legal computation rule: an age is reached the day before the birthday, with a concrete example for people born on the 1st of a month. This is genuinely useful behavioral context beyond what the schema could 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?

The description is compact and front-loaded: the first sentence states the core output, and the second paragraph adds a necessary legal nuance that directly affects correctness. Every sentence earns its place, with no fluff or repetition.

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 simple two-parameter, pure-computation tool with no output schema, the description is complete: it states what is returned (exact dates and premium start/stop), covers all relevant thresholds, and warns about the legal edge case. An agent has enough information to invoke it correctly and interpret its results.

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

Parameters3/5

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 references birth dates in its example but does not add extra meaning about the as_of parameter or the expected date format beyond what the schema already provides. The schema handles parameter semantics adequately.

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 opens with a specific verb and resource: 'Returns the exact date each threshold is crossed and which premium starts or stops.' It lists the exact age thresholds (40/65/70/75) and what happens at each, making the tool's purpose concrete and distinguishable from sibling calculation 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?

The description gives clear usage direction: 'Compute this here rather than by subtracting years.' It explains why this tool is the correct choice by citing the 年齢計算ニ関スル法律 rule. However, it does not explicitly name alternative sibling tools or state when those should be preferred, so it falls just short of full routing guidance.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.