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Torify — Japan Locale APIs for AI Agents

by dev.torify

Japanese Name Romanizer [Torify namespace — official]

torify_name.romanize
Read-onlyIdempotent

Hepburn romanize Japanese names (katakana/hiragana) in passport style. Supports family-first and given-first order. 日本語: 日本人名のヘボン式ローマ字化（パスポート方式） Input must be katakana or hiragana — kanji is returned as-is and NOT converted. To get readings of kanji names, call kanji.toKana first. [Torify namespace — official]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesJapanese name in katakana or hiragana. Kanji input is returned as-is (not converted). Add space between family and given name to split parts. / カタカナ必須・漢字入力時は変換されません。姓名の間にスペースを入れると姓/名を分離して変換
orderNofamily-first: family name first / 姓名順（既定）, given-first: given name first / 名姓順

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes入力値
orderNo適用した名前順序
partsNo姓名に分離できた場合の各パーツ
romajiYesローマ字変換結果（例: SUZUKI ICHIRO）

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already provide readOnlyHint and idempotentHint. Description adds that it only converts kana, not kanji, and returns kanji as-is, plus passport style. No contradictions.

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?

Two sentences in English, Japanese, and a bolded warning. Front-loaded with key info. Very concise, but the bilingual format adds a little length.

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 complexity (2 params, output schema exists), description explains input constraints, output behavior, and call sequence. Output schema covers return values, so no need for more.

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%, so baseline is 3. Description adds context about space splitting, but schema already includes that. No significant additional meaning beyond schema.

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?

Description clearly states 'Hepburn romanize Japanese names (katakana/hiragana) in passport style' and specifies input must be kana, not kanji. Distinguishes from sibling kanji.toKana by telling user to call that first for readings of kanji.

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 says 'Input must be katakana or hiragana — kanji is returned as-is and NOT converted. To get readings of kanji names, call kanji.toKana first.' This clearly defines when to use and when to use an alternative.

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.

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