列出可用视觉模型

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

There are no annotations, so the description carries the full burden. It does disclose the payload shape (provider names, model identifiers, the default), which matters because there is no output schema. It does not explicitly state that the call is side-effect-free, but a configuration listing carries no meaningful ambiguity.

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?

One sentence, front-loaded with the action and resource, then the returned fields. Every clause earns its place, and there is 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-argument lister with no output schema, the description covers what the tool is and what comes back, which is adequate. It could go one step further by saying that the returned provider or model identifiers are the values fed to understand_image, which would close the loop with its sibling.

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 and schema coverage is 100%, so the baseline of 4 applies. There is nothing for the description to disambiguate.

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 uses a specific verb (列出/list) with a specific resource (视觉模型 provider), and it also states the key fields returned: 名称, 模型标识, and 默认使用哪一个. It is clearly distinguishable from the sibling understand_image, which analyzes images, while this tool only enumerates configuration.

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 use case (inspecting which vision providers are configured) is strongly implied, and for a zero-parameter listing tool that is largely sufficient. However, it never states when an agent should call this instead of, or before, understand_image, nor does it note any prerequisites.

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