chatgpt_select_thinking_level

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

With no annotations provided, the description carries the full burden. It discloses that the tool performs a selection and then validates the page result, which is useful. However, it does not mention error handling, return values, or any side effects, leaving gaps in behavioral expectations.

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 main description is a single, front-loaded sentence that directly states the action and validation. The parameter description is also concise and does not pad with unnecessary detail. Every word earns its place.

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 with no output schema, the description is largely complete: it specifies the action, the parameter format, and a recommended prior call. It does not cover failure scenarios or detailed behavior on invalid input, but this is not critical given the tool's simplicity.

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?

Although the schema already covers 100% of the parameter, the parameter description adds significant value: it explains the accepted formats (full displayed name or a numeric string within slider min..max), and recommends listing levels first. This goes beyond the schema's basic string type.

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 a specific action: 'select the thinking level of the current conversation' and also mentions validating the displayed result. This distinguishes it from sibling tools like chatgpt_list_thinking_levels (which lists) and chatgpt_select_mode (which selects a mode).

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 main description implies usage through its action, and the parameter description explicitly recommends calling chatgpt_list_thinking_levels first ('建议先调用chatgpt_list_thinking_levels'). This provides a clear prerequisite, though it does not explicitly list when-not-to-use or alternative exclusion.

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