chatgpt_select_model

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

With no annotations, the description carries the full burden. It does disclose that the tool selects a model and then validates the result ('并校验结果'), and it gives the navigation path. However, it does not explain what happens on failure, whether the previous model selection is overwritten, or what the verification returns, leaving some behavioral 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?

The description is a single densely packed sentence that front-loads the action, includes the relevant navigation path, and states the verification behavior. There is no filler or redundant restatement of the tool name.

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 simple one-parameter tool, the description plus schema provides enough context to call it correctly: what to select, how to identify the model, and the fact that the result is checked. The only notable omission is explicit output or error behavior, but that is a minor gap for this low-complexity selector.

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?

Schema description coverage is 100%, so the baseline is 3. The model parameter is already well documented as the full or unique name shown in the model menu, with a recommendation to call chatgpt_list_models first. The tool description adds no additional parameter-level meaning, so it neither needs to compensate nor adds value here.

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 states a specific action: selecting the model used by the current conversation, following a defined UI path, and then verifying the result. It clearly distinguishes this from sibling selectors such as chatgpt_select_mode, chatgpt_select_thinking_level, and chatgpt_select_answer_tier because the target resource is explicitly the 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 clearly establishes the context: use this when you need to choose the model for the current conversation. The parameter schema adds sequencing guidance by recommending chatgpt_list_models first. It does not explicitly name alternative tools or exclusions, but the model-specific wording makes the intended use obvious among the sibling tools.

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