nim_deep_reason

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It mentions the models used but does not state whether the operation is read-only, what the cost or latency implications are, or what the output looks like—significant gaps for an LLM reasoning tool.

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, front-loaded sentence with zero wasted words. It conveys the core purpose and use cases efficiently.

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 reasoning tool with no annotations and no output schema, the description is minimally adequate: it states what the tool does and when to use it. However, it omits the return format, latency/cost expectations, and guidance on mode tradeoffs beyond what the schema already says, leaving meaningful gaps for an agent.

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 three parameters including the mode enum. The description adds no additional parameter semantics beyond what the schema provides, so baseline 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 states a specific resource (NVIDIA large-scale reasoning models) and use cases (deep system architecture, complex logic, tradeoff analysis), making the tool's purpose clear. However, it does not explicitly distinguish itself from closely related siblings like nim_consultar_modelo or nim_raciocinar_profundo.

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?

It provides clear usage context by naming the problem domains (deep system architecture, complex logic, tradeoff analysis) where the tool should be used. It stops short of stating when not to use it or naming alternatives, which keeps it at 4 rather than 5.

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