laya_status

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

No annotations are provided, so the description carries the full burden. It conveys a read-only status operation via '返す' (returns), but discloses nothing beyond that – no mention of side-effect-free behavior, cost, or what the response contains. For a simple zero-param status tool this is adequate but minimal.

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?

A single front-loaded Japanese sentence with zero waste. Every word earns its place and the core purpose is stated immediately. Appropriate size for a tool with no parameters.

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 zero-parameter tool with an output schema present, the description is nearly complete – it explains what state is returned and the output schema covers the return format. A small gap is the absence of usage context relative to the load/predict lifecycle, but nothing essential to invoking it correctly is missing.

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?

The tool has 0 parameters, so per the rubric the baseline is 4. There is nothing the description needs to add about parameter meaning, and schema coverage is trivially 100% for an empty 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 a specific verb (返す/returns) and resource (ロード済みモデルと実行環境の状態/state of loaded models and execution environment). The purpose is clear and implicitly distinct from siblings laya_load (loading) and laya_predict (prediction), though it does not explicitly name or differentiate them.

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?

Usage context is implied by the name and description – checking environment/model status – but no explicit guidance is given about when to call this versus laya_load or laya_predict. No exclusions, prerequisites, or alternative routing are mentioned.

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