nim_query_model

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

No annotations are provided, so the description carries the full behavioral burden. It does not state whether the call is synchronous, what it returns, whether it has side effects, whether authentication is required, or any rate/cost characteristics; it only says it executes a prompt query.

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 no wasted words. It is appropriately sized, though very minimal and does not build structure beyond the core purpose statement.

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 tool is simple and the input schema is fully documented, but there is no output schema and the description does not explain the return value or response shape. For a model-query tool, an agent would benefit from knowing that it returns generated text and whether response metadata is included.

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 five parameters, including model aliases, defaults, and temperature semantics. The description adds useful model examples but no parameter details beyond what the schema provides, making baseline 3 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 verb and resource: 'Executes a direct prompt query against any NVIDIA NIM model,' with model examples. It is clear what the tool does, though it does not explicitly differentiate itself from specialized siblings like nim_review_code or nim_analyze_vision.

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 'direct prompt query against any NVIDIA NIM model' implies generic model querying, but the description gives no explicit when-to-use guidance, exclusions, or alternatives. An agent can infer that this is the general-purpose sibling, but it must make that inference itself.

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