chatgpt_select_model

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds meaningful context beyond purpose: it describes the internal UI navigation path and states that the tool validates the result, implying it checks for success/failure. This is more transparent than a generic 'select model' statement, though it does not detail side effects or error handling specifics.

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 sentence that front-loads the action ('select the model...'), includes the navigation path, and states the validation step. Every clause earns its place, with no redundant information. It is concise and well-structured.

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?

The tool is simple with one parameter and no output schema. The description explains the core action and validation, but it does not clarify what the validation returns (e.g., success status, error message) or how failures are surfaced. Given the lack of output schema and annotations, the description leaves some ambiguity about the tool's observable behavior.

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 input schema has 100% description coverage for the single 'model' parameter, including guidance to use the full or unique name from the menu and to call chatgpt_list_models first. The main description adds no additional meaning beyond the schema, so the baseline score of 3 applies.

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 the tool's specific action: selecting the model for the current conversation, and even provides the UI navigation path ('current tier → advanced → model') and a validation step. This distinguishes it from sibling tools like chatgpt_select_mode or chatgpt_list_models by focusing on model selection with a concrete mechanism.

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 is used when you want to change the model for the current conversation, and the schema parameter suggests calling chatgpt_list_models first. However, the main description does not explicitly state when to use this tool versus alternatives (e.g., select_mode, select_thinking_level) or provide exclusion criteria. Usage context is present but not fully explicit.

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