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Torify — Japan Locale APIs for AI Agents

by dev.torify

Japanese Postal Code Lookup

postal.lookup
Read-onlyIdempotent

Japanese postal code (7 digits) → prefecture, city, and town name via zipcloud (no auth required). 日本語: 郵便番号 → 都道府県・市区町村・町域 Use to resolve any Japanese postal code to its real address — do not guess from memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipcodeYes郵便番号（7桁、ハイフン任意。例: 1000005 または 100-0005）

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kana1YesPrefecture name in katakana / 都道府県名（カタカナ）
kana2YesCity/ward/town name in katakana / 市区町村名（カタカナ）
kana3YesDistrict name in katakana / 町域名（カタカナ）
cityEnNoCity/ward name in English (romanized) / 市区町村名（英語・ローマ字）
townEnNoDistrict name in English (romanized) / 町域名（英語・ローマ字）
zipcodeYesPostal code (7 digits) / 郵便番号（7桁）
address1YesPrefecture name / 都道府県名
address2YesCity/ward/town name / 市区町村名
address3YesDistrict/neighborhood name / 町域名
prefcodeYesPrefecture code / 都道府県コード
prefectureEnNoPrefecture name in English (e.g. Tokyo) / 都道府県名（英語）

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description adds that it uses zipcloud (external API) and requires no auth, providing useful behavioral context beyond 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?

The description is two sentences plus a Japanese note, front-loading the key purpose and usage. Every sentence is necessary and concise.

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 the single parameter, high schema coverage, and existence of an output schema, the description fully covers what the tool does and how to use it.

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 a detailed description of the zipcode parameter (format, example). The description adds only minor context (7 digits), so 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?

The description clearly states the tool resolves Japanese postal codes to prefecture, city, and town names, with a specific instruction not to guess. This differentiates it from sibling tools like geo.geocode.

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 explicitly says 'Use to resolve any Japanese postal code to its real address — do not guess from memory,' providing clear usage guidance. No alternatives are mentioned, but the context is sufficient.

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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