nv_route

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

No annotations are present, so the description carries the burden of disclosing behavioral traits. It does disclose the critical trait that the tool does not call the models, but it lacks details on return format, error handling, authentication, or any service dependencies, leaving gaps for an agent.

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 remarkably concise: a single purpose sentence followed by a structured 'Args' section. There is no filler, and each sentence conveys essential information, making it easy to parse quickly.

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?

For a simple 2-parameter tool with an output schema, the description covers the core purpose, parameter semantics, and a key behavioral nuance. It could clarify the relationship between 'models' and 'categories' but remains sufficiently complete for this complexity level.

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 explains both parameters: 'task' as a free-text description with English/Turkish support and examples, and 'top_k' as the number of candidate categories to return. This adds meaningful behavioral context beyond the schema's bare titles and defaults.

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 operation ('Recommend') on a specific resource ('the best NVIDIA models') and for a task, explicitly noting it does so 'without calling them.' This contrasts with sibling tools like nv_call and nv_list_models, providing a distinct purpose.

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 for model recommendation without execution, but it does not explicitly mention when to prefer this tool over alternatives or provide exclusions. The task examples give some context, but no direct alternative guidance is given.

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