laya_load

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'preload' without revealing whether the model is downloaded (network dependency), whether it becomes active for laya_predict, whether it replaces an existing loaded model, or what the response contains. A mutation-like operation with zero annotation coverage should disclose more.

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 with no wasted sentences: the purpose is front-loaded in the first line, followed by a tight arg list. It earns its space without padding.

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 2-optional-param load tool with an output schema present, the description covers purpose and all parameter semantics. However, with zero annotations it omits behavioral context (network, activation, replace semantics), leaving the completeness short of strong.

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 description coverage is 0%, so the description must compensate – and it does. Both parameters are documented with type meaning (Hugging Face ID or local path), an example value, and dtype defaults (float16/float32). This adds real value beyond the bare schema names.

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+resource ('チェックポイントを事前ロードする' – preload checkpoint) and notes the default selection behavior (env var or multilingual when unspecified). Sibling differentiation (preload vs. status vs. predict) is only implicit from the names, not stated explicitly.

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 rather than stated – a preload step naturally precedes laya_predict, but the description never says 'use before prediction' nor contrasts with laya_status/laya_predict. No when-not or alternative guidance is provided.

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