Get random onomatopoeia

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

Annotations already cover readOnly and non-destructive behavior. The description adds transparency by stating the random nature, count range (1-10), category filtering, and the return structure. It also provides example category 'taste', giving a peek into expected behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is compact and well-organized with clear sections for Args, Returns, and Examples. It front-loads the core purpose and avoids unnecessary filler, making it easy to scan and apply.

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

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

For a simple two-parameter tool with annotations and an output schema, the description covers everything needed: purpose, parameter constraints, return format, and usage examples. It is complete without being verbose.

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

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

Schema coverage is 100%, but the description reinforces parameter semantics with concrete examples (e.g., '食感の語で3問クイズ' -> count=3, category='taste') and states defaults. This adds meaningful interpretive guidance beyond the schema.

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

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

The description states 'ランダムにオノマトペを取り出す' (pick random onomatopoeia entries) and provides concrete use cases like quizzes and daily-word features. This clearly identifies the tool as a random selection utility, distinct from siblings like search or explain.

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

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

It specifies useful contexts ('daily-word features and quizzes') and gives example queries that map to parameter values. However, it does not explicitly mention when to use search or browse tools instead, so it lacks explicit alternatives/exclusions.

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