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nv_categories

Discover all task categories that the NVIDIA catalog supports, each with its top models. Use this to identify the right model for reasoning, coding, vision, embeddings, and translation tasks.

Instructions

List every task category this server can route to, with its top models.

Start here when you want to know what the NVIDIA catalog is good for.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It conveys that the tool is a read-only listing operation and that it returns categories with models, but it does not disclose potential limitations, pagination, or response details. For a simple list tool, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences with no wasted words. The first sentence states the core function, the second gives practical guidance. It is perfectly sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and an output schema exists, the description adequately covers the purpose, usage context, and expected output. It could optionally mention whether the list is exhaustive or sorted, but 'every task category' implies completeness. The description is sufficient for a straightforward listing tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so the description's lack of parameter documentation is acceptable. The baseline of 4 is appropriate because no parameter explanations are needed and the description focuses on the tool's purpose rather than input specifics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('List every task category') and resource ('this server can route to'), and adds context ('with its top models'). It distinguishes itself from siblings like nv_list_models and nv_route by framing itself as the starting point for understanding the catalog.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Start here when you want to know what the NVIDIA catalog is good for' gives clear usage context, implying this is the discovery tool. It does not explicitly mention alternatives or when not to use it, but the 'start here' guidance effectively positions it among siblings.

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

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