ChatGPT

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, and the description adds real context: one prompt per call, no conversation history, and that `search` enables ChatGPT's web search. It omits anything on latency, rate limits, error behavior, or the shape of the returned JSON beyond 'parsed as JSON'.

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?

Three short sentences, front-loaded with the core action and the single-turn constraint, followed by the usage example and a docs link. Nothing is padded or redundant.

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?

With no output schema, the description does state the return type ('answer parsed as JSON') and the one-shot interaction model, plus a docs link for deeper detail. It could say more about the response envelope, but nothing essential for calling the tool is missing.

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 all three parameters are already documented. The description reinforces `search` ('set `search` to let ChatGPT use web search') but adds nothing for `geo`, so it only marginally exceeds the schema baseline.

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?

States a specific verb and resource: send one prompt to ChatGPT via the Decodo Web Scraping API and return the answer parsed as JSON. It is clear about the single-turn nature of the call, but it never distinguishes itself from sibling LLM-answer tools like perplexity or google_ai_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?

Gives a concrete use case ('see how ChatGPT answers a question... check how it describes a brand') and a key constraint (no conversation history), which sets expectations about repeated calls. It stops short of naming alternatives or stating when not to use it versus the other AI-answer siblings.

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