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Torify — Japan Locale APIs for AI Agents

by dev.torify

Kanji to Kana Converter

kanji.toKana
Read-onlyIdempotent

Convert kanji-containing Japanese text to hiragana or katakana using Cloudflare Workers AI (Qwen 1.5 14B, Japanese/Chinese optimized). 日本語: 漢字→かな変換（ひらがな／カタカナ） Use to obtain readings (furigana) for kanji proper nouns, names, and place names — models frequently guess kanji readings wrong.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes変換するテキスト（最大500文字）
outputNo出力形式（既定: hiragana）

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kanaYesひらがな/カタカナ変換結果
inputYes入力テキスト
outputYes出力形式（hiragana または katakana）
sourceYes変換エンジン

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), description reveals it uses Cloudflare Workers AI (Qwen 1.5 14B) and that the model may inaccurately guess readings. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences in English, one in Japanese. No redundant words. Key information front-loaded: purpose, model, usage guidance.

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 tool complexity (AI-based conversion with potential inaccuracies) and presence of output schema, description provides all necessary context: function, use case, limitation, and format. No gaps.

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 has 100% coverage with descriptions for both parameters. Description repeats the output format enum values but adds no additional detail beyond schema. Baseline 3 is appropriate.

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?

Clearly states conversion of kanji-containing Japanese text to hiragana/katakana, explicitly for obtaining readings of proper nouns, names, and place names. Distinct from sibling tools like name.romanize (romaji) and wareki.convert (date conversion).

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 recommends use for kanji proper nouns, names, and place names, and warns that models frequently guess readings wrong. Provides both English and Japanese instructions.

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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