get_model_list

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

With no annotations provided, the description fully carries the behavioral transparency burden. It explains the return data structure (list of objects), the meaning of each field (model, coefficient, locked), and the billing implications (coefficient 3 consumes 3x quota). It stops short of discussing potential errors or authorization details, but for a read-only list tool this is strong context.

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, information-dense, and every sentence earns its place. It front-loads the purpose, then gives workflow ordering, return field semantics, billing behavior, and a warning about locked models—all without redundancy.

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?

Given the tool has no parameters and no output schema, the description compensates fully by explaining the return data structure and field meanings. It also provides enough workflow context (call before translate_document, let user choose) for the agent to use the tool effectively. No critical missing information.

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 zero parameters, so the baseline is 4. The description does not need to elaborate on parameters since none exist, and the schema already reflects that. No additional parameter-level context is required or possible.

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 ('获取') and resource ('翻译模型列表'), and further distinguishes itself by explicitly tying its usage to the translate_document workflow. It is clearly separated from sibling tools like get_supported_languages because it is about model selection for translation, not just languages.

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?

The description gives explicit usage guidance: call this tool before translate_document, have the user pick a model, relay the coefficient (billing multiplier), and avoid locked models for submission. This covers when to use, how to present results, and what not to do, leaving no ambiguity for the agent.

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