chatgpt_select_thinking_level

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

No annotations are provided, so the description carries the full burden. It does disclose that the tool selects the level and then verifies the displayed result, which adds behavioral insight. However, it does not mention side effects, possible failure modes, or whether the page must already be in a particular state, so transparency is partial.

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 concise sentence that front-loads the core action and adds the verification step. Every clause earns its place; there is no repetition or 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 single-parameter UI action with no output schema, the description plus schema is nearly complete: it states the action, the verification behavior, and how to determine the value to pass. It does not specify return/error details, but the tool's simplicity limits the impact of that omission.

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%, and the schema already explains that thinkingLevel should be the displayed name or a numeric string for slider controls, and recommends calling chatgpt_list_thinking_levels first. The main description adds no parameter-level meaning beyond the schema, so the baseline score of 3 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 uses a specific verb ('选择' / select) with a clear resource ('ChatGPT 网页当前对话的思考强度' / the current conversation's thinking level on the ChatGPT web page) and adds verification behavior ('并校验页面显示的结果'). This clearly distinguishes it from sibling tools like chatgpt_list_thinking_levels, chatgpt_select_mode, 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 parameter description provides explicit guidance to call chatgpt_list_thinking_levels first and explains when a numeric string is acceptable (when the control is a slider). It does not explicitly state when not to use this tool versus select_mode/select_model, but the context is clear enough for correct usage.

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