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Torify — Japan Locale APIs for AI Agents

Japanese Era Date Converter

wareki.convert
Read-onlyIdempotent

Convert between Japanese era dates (wareki) and Gregorian. Supports Meiji/Taisho/Showa/Heisei/Reiwa. Handles era boundary dates accurately. 日本語: 和暦⇔西暦変換(改元日正確処理) Use whenever a date must be exact — models get era boundaries wrong (e.g. Showa 64 = Jan 1-7 1989 only). Do not compute wareki from memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNoDay 1-31 / 日(1〜31)
eraNoEra name (Reiwa/Heisei/Showa/Taisho/Meiji or 令和/平成/昭和/大正/明治)
dateNoGregorian date YYYY-MM-DD, required when direction=g2w / 西暦日付(direction=g2w 時必須)
monthNoMonth 1-12 / 月(1〜12)
eraYearNoEra year (1 or greater) / 元号年(1以上)
directionYesg2w: Gregorian to wareki / 西暦→和暦, w2g: wareki to Gregorian / 和暦→西暦

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNo日(w2g 時)
eraNo元号(例: 令和)
yearNo西暦年(w2g 時)
monthNo月(w2g 時)
eraYearNo元号年
eraRomajiNo元号ローマ字(例: Reiwa)
formattedNoフォーマット済み日付文字列
eraYearLabelNo元号年ラベル(例: 元年)

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds crucial context about accurate handling of era boundary dates, which is a behavioral promise beyond what annotations convey. No contradiction with annotations.

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 English sentences, a Japanese sentence, and a bolded usage note. It is relatively concise and front-loads the purpose and usage guideline. However, the Japanese text may be redundant for some agents, slightly reducing conciseness.

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?

Given an output schema exists (not shown but mentioned), the description covers purpose, supported eras, accuracy, and usage context. No further information needed for an AI agent to correctly invoke this tool.

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 coverage is 100% with descriptions for all 6 parameters. The tool description itself does not add additional parameter semantics beyond what is in the schema. Baseline 3 is appropriate as the description does not deepen understanding of parameters.

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 the tool converts between Japanese era dates and Gregorian, lists supported eras (Meiji/Taisho/Showa/Heisei/Reiwa), and emphasizes accuracy at boundary dates. This uniquely distinguishes it from sibling tools like geo.geocode or name.romanize.

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?

Explicitly advises to use whenever a date must be exact and warns against computing wareki from memory, providing a concrete example (Showa 64). This gives clear when-to-use and when-not-to-use context, with no alternative tools suggested but sufficient for this niche.

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

A3.7/5.0
Disambiguation1/5

The tool set contains exact duplicates for 10 tools (e.g., company.fullProfile and torify_company.fullProfile). An agent cannot distinguish between them, leading to confusion and potential misselection. The purpose of having both namespaced and non-namespaced versions is unclear.

Naming Consistency2/5

Naming is inconsistent: some tools use a dot-separated namespace (e.g., company.fullProfile), while others have a prefix (torify_company.fullProfile). Some tools lack the prefix (e.g., geo.geocode) while their duplicates have it (torify_geo.geocode). No consistent verb_noun pattern; naming conventions are mixed.

Tool Count2/5

With 24 tools, the count is high, and half are duplicates. The effective unique tool count is 12, which is reasonable for the domain, but the duplication inflates the count unnecessarily, making the surface seem bloated and confusing.

Completeness4/5

The tool set covers key Japanese locale operations: corporate lookup, invoice validation/verification, geocoding, postal lookup, name romanization, kanji conversion, law search, and wareki conversion. However, there are no tools for updating or deleting data, which is expected since these are read-only or verification APIs. Minor gaps like missing station/landmark geocoding are noted but acceptable.