complete

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

No annotations provided, so description carries full burden. Mentions OpenAI compatibility and raw text-completion, but does not detail return type, side effects, or safety considerations. Output schema may cover return type, but behavioral traits like idempotency or rate limits are absent.

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 concise sentences plus an example. Every sentence adds value, and the purpose is front-loaded. No redundant information.

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?

Provides clear purpose and usage guidance, but lacks details on authentication, rate limits, and behavior of the stream parameter. However, the existence of an output schema partially compensates for missing return value explanation.

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 0%. The description only explains model, prompt, and max_tokens via example, leaving stop, top_k, top_p, stream, and temperature undocumented. Since most parameters are not described, the description does not sufficiently compensate for the lack of schema descriptions.

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?

Clearly states it sends a raw text-completion request to a model, compatible with OpenAI /v1/completions. Differentiates from sibling chat() by specifying complete() is for base/completion models or prompt-template experimentation.

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?

Explicitly advises to prefer chat() for instruction-tuned models and states the appropriate use cases for complete(). Provides an example with typical parameters.

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