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Torify — Japan Locale APIs for AI Agents

by dev.torify

Japanese Postal Code Lookup [Torify namespace — official]

torify_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. [Torify namespace — official]

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 already indicate readOnlyHint, openWorldHint, and idempotentHint, so the description adds value by specifying the external API (zipcloud) and no authentication needed. It does not disclose error handling or rate limits, but the annotations cover the core safety profile adequately.

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: two lines in English and Japanese plus an emphasized instruction. Every sentence is purposeful, with no redundant information, and the key action is front-loaded.

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 lookup tool with one parameter, full schema coverage, rich annotations, and an output schema, the description is complete. It names the external service and confirms read-only, idempotent behavior, leaving no critical 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?

The input schema already provides a complete description of the zipcode parameter (7 digits, optional hyphen, examples). The description reiterates the format but does not add new semantic meaning beyond what the schema provides, resulting in a baseline score of 3.

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 specifies the tool's function: converting a Japanese postal code to prefecture, city, and town name. It includes the data source (zipcloud) and authentication requirement, and differentiates itself from sibling tools via the Torify namespace.

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 instructs to use the tool for resolving postal codes and advises against guessing from memory, which serves as a usage guideline. However, it does not compare with sibling tools like postal.lookup or geo.geocode, leaving some ambiguity about when to use alternatives.

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

All Torify — Japan Locale APIs for AI Agents toolsInspect
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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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