chatgpt_select_mode

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

With no annotations, the description carries the behavioral burden; it does disclose a post-selection verification behavior. However, it does not mention side effects of switching modes, error/failure behavior, or whether an active browser page is required.

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?

One tightly worded sentence delivers the action, target, examples, and verification behavior with no filler or repetition. The most decision-relevant information is front-loaded.

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 tool with no output schema, the combination of description and input schema covers the selection workflow: list modes, provide the displayed mode name, select, and verify. Minor omissions such as failure handling and browser prerequisites are not material for this simple UI action.

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 coverage is 100% so the baseline is 3, and the description adds practical meaning beyond the schema by clarifying that mode is a top-page label and providing examples. It reinforces the schema's suggestion to use chatgpt_list_modes, helping the agent supply a valid string.

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 action ('选择') and resource ('ChatGPT 新版页面顶部模式'), gives concrete examples ('聊天' or '工作'), and adds a verification step ('校验选中状态'). This clearly distinguishes it from sibling selectors such as chatgpt_select_model and chatgpt_select_project.

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?

It identifies the relevant domain (top-page modes) and explicitly recommends calling chatgpt_list_modes first, which gives an actionable precondition. It does not explicitly state when not to use it, but the examples and wording make the intended use case clear.

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