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Explain a Japanese onomatopoeia

jp_explain_onomatopoeia
Read-onlyIdempotent

Explain the meaning, category, example sentence, and nuance of any Japanese onomatopoeia by entering the word in hiragana, katakana, or romaji.

Instructions

特定のオノマトペの意味・分類・例文・使い分けのニュアンスを解説する。

Explain the meaning, category, example sentence, and nuance of a specific Japanese onomatopoeia.

Args:

  • word (string): 見出し語またはローマ字。例: "しんしん", "shinshin"

Returns: { "found": boolean, "entry": { "word", "romaji", "type", "categories", "meaning_ja", "meaning_en", "example_ja", "example_en", "nuance" } | null, "message": string }

Examples:

  • 「もふもふってどういう意味?」 -> word="もふもふ"

  • "What does dokidoki mean?" -> word="dokidoki"

Error Handling:

  • 収録されていない語の場合は found=false を返し、jp_search_onomatopoeia の利用を案内する

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordYes解説したいオノマトペ(ひらがな・カタカナ・ローマ字)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
entryYes
foundYes
messageYes
Behavior5/5

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

The description discloses the return format (found boolean, entry structure with fields, message) and error behavior (found=false when not in dictionary). It goes beyond the annotations by describing the not-found fallback and example input formats, which aids the agent in predicting tool behavior.

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 well-structured with clear sections: intro, args, returns, examples, and error handling. It is slightly verbose due to bilingual repetition, but every section serves a purpose and the front-loaded English explanation makes the core function immediately clear.

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 tool's simplicity (one parameter) and the presence of an output schema, the description fully covers usage: what to pass, what to expect in the return object, and how to handle failures. The examples and error handling make it complete for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully describes the 'word' parameter with min/max length and a concise description. The description's Args section adds value by providing example values ('しんしん', 'shinshin') and clarifying that both kana and romaji are accepted, which enriches the schema's baseline meaning.

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's function: explaining the meaning, category, example, and nuance of a specific Japanese onomatopoeia. The verb 'explain' and resource 'specific Japanese onomatopoeia' distinguish it from siblings like jp_search_onomatopoeia, which is used for searching when a word is not found.

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 includes an 'Error Handling' section that explicitly instructs the agent to use jp_search_onomatopoeia when the word is not in the dictionary. It also provides examples of input patterns, implying the tool is for explaining known onomatopoeia. However, it does not explicitly contrast its use with other siblings like jp_browse or jp_random.

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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