chatgpt_list_models

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

With no annotations, the description carries the behavioral disclosure burden. It explains that the list is dynamic and reflects actual availability, and it explicitly prohibits guessing model names — a meaningful behavioral guardrail. It does not mention read-only status or failure behavior, but for a listing tool the key behavior is well covered.

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 and front-loaded: it names the purpose, scope, navigation path, and the critical 'do not guess' rule in two short sentences. Every clause adds decision-relevant information with 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-parameter list tool with no output schema, the description adequately covers data source, scope, and a key constraint. It does not describe the return shape or failure scenarios, but these are largely inferable from the tool name and listing purpose.

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 the schema description coverage is 100%, so there are no parameter semantics for the description to add. Per the 0-parameter baseline, 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 specifies the action ('列出' / list), the resource (models actually available for the current ChatGPT account), and the navigation source ('当前档位→高级→模型'). It also distinguishes itself from siblings like list_modes and select_model by focusing on model availability rather than modes or selection.

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 the tool should be used when the agent needs to discover which models are actually available for the current account, but it does not explicitly state when to prefer this over chatgpt_list_modes, chatgpt_capabilities, or chatgpt_select_model. There is no 'use when' / 'use instead' guidance.

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