completion

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

Discloses key behaviors: quick completion, model selection, temperature warning. However, does not mention response format, error handling, or whether it's synchronous. With no annotations, slightly lacking but still good.

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?

Front-loaded with purpose, then usage guidelines, then parameter details. Slightly lengthy but every sentence adds value. Could be more concise, but 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?

Covers model selection, parameter details, sibling differentiation. Missing expected response format or error states, but output schema exists and not needed. Adequate for a completion tool.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: model (full ID or shorthand), prompt, system, temperature (range, default, warning). Adds meaningful context beyond schema titles.

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 'Call a model for a quick completion' with a specific verb and resource. It distinguishes from sibling tool start_research by contrasting quick vs deep research tasks.

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 says when to use ('standard prompts that return in seconds') and when not to (use start_research instead). Provides guidance on model selection using shorthands or search_models.

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