list_models

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

With no annotations, the description carries the full burden: 查询 signals a read-only operation, and the field list (ID, install status, advanced parameters) tells the agent what comes back. It says nothing about auth requirements, whether uninstalled models are included, or result ordering/size.

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 compact sentence with the query scope front-loaded and the usage note after the semicolon. Dense but every clause carries information; no filler.

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-param listing tool with no output schema, the description does the necessary work by naming the returned fields and tying the call to the generation workflow. Output format and filtering behavior remain unspecified but are minor for a simple enumeration.

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 takes zero parameters, so the baseline is 4. The description correctly implies a no-argument listing call and doesn't invent filtering options that don't exist.

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?

Specific verb (查询) plus resource (模型) and it enumerates the returned fields: model ID, install status, advanced parameter definitions. It implicitly separates itself from list_voices (voices vs models) and points at generation, though it never names the sibling tools outright.

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?

'生成前选择已安装模型' gives a clear usage context – call this before generate to pick an installed model. It states when to use it but offers no exclusions or explicit alternative-tool routing.

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