list_models

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'List' which implies a read-only operation, but it does not disclose return format, pagination behavior, authentication requirements, rate limits, or the fact that 'detailed metadata' is only returned when include_details=true. The description adds little beyond the obvious read-only nature.

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 a single, well-structured sentence that conveys purpose, filtering capability, and target use case without any fluff or repetition. Every word earns its place.

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?

Despite having 10 optional parameters, no output schema, and no annotations, the description is very brief. It does not mention what metadata is actually returned, whether results are paginated, how to handle missing parameters, or any defaults beyond what the schema provides. The tool has moderate complexity and the description leaves significant gaps.

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%, so the schema already documents all 10 parameters. The description adds minimal extra meaning by mentioning 'category' and listing example categories like language, embedding, reranking, etc., but this is largely redundant with the schema's enum. It also refers to 'advanced filters' without detailing them, adding little semantic value.

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 uses a specific verb-resource combination: 'List available NVIDIA NIM models'. It clearly states the scope (available models) and mentions filtering by category, distinguishing it from sibling tools like get_model_info (which presumably retrieves details for a specific model) and compare_models.

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 phrase 'for agent selection' provides some context for when the tool might be appropriate, implying it is used during model discovery. However, it does not explicitly state when to use it instead of alternatives like get_model_info or compare_models, nor does it mention any exclusions or prerequisites.

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