Recommend Wiro models

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. Beyond those, the description adds the critical behavioral detail that the tool returns a `get_model_schema` next action, guiding the agent's workflow. This enriches the annotation coverage, though not exhaustively.

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?

Two sentences, highly efficient. The main purpose is front-loaded, and the follow-up action is stated crisply. No wasted words.

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 an output schema present, the description need not detail return values. It explains the purpose and the immediate next step. It lacks explicit mention of when to avoid this tool (e.g., when a specific model name is known), but that is a usage-guideline gap rather than a completeness gap. Overall adequate for a recommendation 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?

The schema covers the single 'task' parameter with a clear description and example. The tool description reiterates 'natural language' but does not add significant new meaning beyond the schema. Baseline 3 is appropriate given full schema coverage.

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 states a specific verb ('recommend') and resource ('models') based on a natural language goal, clearly distinguishing it from siblings like search_models or get_model_schema. It explains the purpose without ambiguity.

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 implies usage timing ('before running a model') and mentions the next action, but does not explicitly contrast with alternatives like search_models or state when NOT to use this tool. The guidance is present but minimal.

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