chatgpt_list_models

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It honestly discloses dynamic behavior, reliance on page hierarchy, and the constraint not to guess model names. It does not mention return format or error conditions, but for a list operation this is reasonably transparent.

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 two concise sentences with no redundant phrases. It front-loads the purpose and includes a practical usage note ('不要猜测模型名称'), making every word useful.

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 tool with no output schema, the description provides adequate context: it explains the source of truth (page hierarchy) and the current-account scope. It could have mentioned return format or prerequisites (e.g., browser open), but the essentials are covered.

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 and schema coverage is trivially 100%. The description adds no parameter details because there are none to explain. Per the baseline for 0-param tools, a score of 4 is appropriate.

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 clearly states the tool lists models actually available for the current ChatGPT account, using a specific page hierarchy. It uses a specific verb ('列出' / list) and resource ('模型' / models), and distinguishes itself from sibling tools like chatgpt_list_modes and chatgpt_select_model.

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 implies when to use this tool: to discover real, available model names rather than guessing. It provides the context of the page hierarchy ('当前档位→高级→模型') but does not explicitly name alternatives or exclusion conditions, so it falls short of a 5.

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