pplx_nemotron_thinking

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

With no annotations, the description must cover behavioral traits. It mentions extended thinking (implying deeper reasoning) and cost, but lacks details on response format, side effects, permissions, or rate limits.

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 very short, but it omits critical information. It is not a model of conciseness if it fails to adequately describe the tool; however, it does front-load the key differentiator (model and cost).

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?

Given 3 parameters with no schema descriptions and no annotation support, the description is insufficient for an agent to use the tool correctly. Output schema exists but is not described, and parameters are not explained.

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%, yet the description adds no explanation for the three parameters (query, source_focus, conversation_id). Users must infer their meaning from names alone, which is insufficient.

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 tool uses NVIDIA's Nemotron 3 Ultra 550B model with extended thinking and notes it costs one Pro Search query. This distinguishes it from sibling tools like pplx_query or pplx_claude_opus by specifying the model and thinking feature.

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?

No guidance is provided on when to use this tool versus alternatives like pplx_gpt55_thinking or pplx_smart_query. The only hint is the cost implication, but no exclusion criteria or context for selection.

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