chatgpt_list_modes

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

With no annotations, the description carries the behavioral transparency burden. It indicates the result is dynamic ('当前可用') and implies a read-only listing operation, but it does not explicitly state that it has no side effects or describe the return format. For a simple list tool the risk is low, but some behavioral detail is still missing.

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 a single focused sentence that front-loads the operation and resource, with two clarifying examples. No filler or redundant wording.

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, no-output-schema listing tool, the description covers the essential information: what is listed, where, and with examples. It could mention what the returned values look like or whether it reflects live page state, but it already says '当前可用,' making it reasonably complete.

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 per the rubric the baseline is 4. There are no parameter details to clarify, and the description does not need to compensate for an input schema gap.

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 names a specific verb ('列出'), a precise resource ('ChatGPT 新版页面顶部当前可用的模式'), and gives concrete examples ('聊天' and '工作'). This clearly distinguishes listing modes from listing models or selecting a mode among the sibling tools.

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 context is clear: this tool enumerates currently available modes, implying it should be used before selecting a mode. It does not explicitly name alternatives like chatgpt_select_mode, but the usage context is strong enough for an agent to infer when to call it.

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