openai_recommend_model

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

No annotations are supplied, so the description carries the full burden. It does disclose something useful: the recommendation is derived from a live model list plus curated priorities, implying the output shifts as availability changes and is not a static hardcoded mapping. It does not state that the operation is read-only, whether it makes network calls, or any rate/latency characteristics.

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?

A single front-loaded sentence with no filler and no redundancy. It is efficient, though the terseness is partly responsible for the missing parameter and usage detail.

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?

An output schema exists, so return values need not be explained, and the tool is simple with only two parameters. However, for a recommendation tool the domain of valid 'task' values and the accepted 'budget_preference' values are the core information an agent needs and are entirely absent.

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%, so the description must compensate and it does not: neither 'task' nor 'budget_preference' is explained. Critically, budget_preference has a default of 'balanced' but no enum or listed valid values, and 'supported task' is undefined, leaving the agent to guess acceptable inputs.

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 names a specific verb and resource ('recommend an available model') and adds the mechanism ('live model list and curated priorities'), which distinguishes it from siblings like openai_list_models and openai_get_model. The only dilution is the phrase 'for a supported task', which doesn't define what a supported task is.

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?

Usage is only implied: an agent can infer this is the tool to call when choosing which model to use, but there is no explicit statement of when to prefer it over openai_list_models or openai_get_model, and no prerequisites or exclusions are given.

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