Recommend Wiro models

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable behavioral context by specifying the natural language input requirement and the fact that a next action is returned, which is not evident from annotations alone.

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 two sentences long, front-loaded with the core function and then a critical directive. No unnecessary words or repetition of schema details, making it highly efficient.

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 the tool's simplicity (1 parameter, full schema coverage), the presence of an output schema, and strong annotations, the description is largely complete. It covers the tool's purpose, input type, and post-usage workflow. Some minor details about ranking criteria or output structure are omitted, but these are likely covered by the output schema.

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 100%, with the 'task' parameter fully documented via examples. The description itself does not add further parameter semantics beyond what the schema provides, so the baseline of 3 is appropriate.

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's purpose: 'Describe a generation goal in natural language and receive ranked model recommendations.' It uses a specific verb and resource, and distinguishes itself from siblings like search_models (which likely searches by criteria) and get_model_schema (which retrieves schemas).

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 a clear usage instruction: 'Follow the returned get_model_schema next action before running a model,' guiding the agent on the recommended workflow. It lacks explicit alternatives or when-not-to-use conditions, but the context is clear enough for proper usage.

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