AI chat completion

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

Beyond having no annotations, the description discloses cost (1 credit base), no multipliers, caching behavior (expiration), and localization features. It does not detail rate limits or authentication requirements, but the provided information is sufficient for basic understanding.

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 concise, using short sentences and bullet-like lists to convey key points. It avoids redundancy and is well-organized for quick consumption, though it could be slightly more 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?

Given 9 parameters and no output schema, the description covers most aspects (model, caching, localization) but omits details on the output format beyond 'structured sources'. It meets minimum viability but leaves some ambiguity about return value structure.

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 schema covers 100% of parameters with individual descriptions, but the tool description adds value by explaining expiration semantics, localization context, and cost implications. This enrichment goes beyond the schema's 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?

The description clearly states the tool gets a chatbot answer from specific models (ChatGPT, Gemini, Perplexity, Copilot) and mentions structured sources. This distinct purpose is well-differentiated from sibling tools like 'account_status', 'web_fetch', and 'web_search'.

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 provides guidance on using expiration for fresh vs cached responses and mentions localization parameters. However, it does not explicitly contrast with sibling tools or state when not to use this tool, though the distinct purpose makes such guidance less critical.

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