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

There are no annotations, so the description carries the full disclosure burden. It does this well by revealing an external runtime dependency on Ollama, the required model, default host, and environment-variable overrides, plus domain auto-detection. It stops short of describing failure modes or latency, but the core behavioral traits are meaningfully disclosed.

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 core purpose is front-loaded, followed by compact, well-organized dependency and argument sections. The code block and argument list are efficient, and every line adds operational value, including defaults for the model and host overrides.

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?

The description covers the essential invocation path: prerequisites, environment configuration, and both parameters. Since an output schema exists, return-value documentation is not required here. The only notable gap is lack of sibling-routing guidance among the eight related tools, but the tool is still safely callable with this description.

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 0%, and the description fully compensates. It defines question as a natural-language question about the NVIDIA AI stack and explains that domain comes from list_domains(), is auto-detected if omitted, and only needed for disambiguation. This gives the agent real semantics beyond the bare schema.

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 first sentence clearly states a specific verb-resource pair: 'Ask a natural-language question answered by Qwen grounded on the NVIDIA CKG.' This is unambiguous about what the tool does and is distinct from the sibling names, though it does not explicitly name an alternative to differentiate against.

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 gives a concrete prerequisite ('Requires Ollama running locally with a Qwen model pulled') and explains that domain is auto-detected when omitted. It does not, however, say when to prefer ask_nvidia over siblings like query_ckg or route_query, nor does it provide explicit when-not-to-use guidance.

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