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kaomoji

Find Japanese text emoticons by emotion or keyword for inline message expressions. Filter categories like happy, sad, or table-flip to match your tone.

Instructions

Get a kaomoji (Japanese text emoticon) by emotion or keyword. Perfect for inline text expressions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoEmotion or keyword (e.g. "happy", "sad", "cat", "shrug"). Omit for random.
categoryNoFilter by category (e.g. "happy", "animals", "table-flip")
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to specify whether the tool returns a single kaomoji or a list, what happens when no match is found, or whether the operation is idempotent. The phrase 'Get' implies a read-only operation but lacks explicit safety guarantees.

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 consists of two efficient sentences with zero waste. The first sentence front-loads the core functionality (getting kaomoji by emotion/keyword), while the second provides concise contextual value (inline text expressions). Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema (two optional strings) and lack of output schema, the description adequately covers the tool's purpose but leaves a gap regarding return value structure. For a tool with no annotations and no output schema, it should ideally specify what the tool returns (e.g., a string, an object with the kaomoji, etc.).

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?

With 100% schema description coverage, the schema already fully documents both 'query' and 'category' parameters. The description mentions 'emotion or keyword' which aligns with the query parameter, but adds no additional semantic value regarding input formats, valid values, or parameter interaction beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Get[s] a kaomoji (Japanese text emoticon)' with specific verb and resource. It distinguishes the specific domain (kaomoji) from generic siblings like 'get' or 'search', though it doesn't explicitly differentiate from the 'random' sibling which may overlap when parameters are omitted.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a use-case hint ('Perfect for inline text expressions') but offers no explicit guidance on when to use this versus siblings like 'random' or 'categories', nor does it clarify when to use 'query' versus 'category' parameters or what happens if both are omitted.

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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