get_gpu_utilization

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by specifying the output format ('Markdown report of GPU utilization per node'), prerequisites, and monitoring scope. It doesn't mention rate limits or auth needs, but covers key operational aspects adequately for a read-only monitoring 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 well-structured with clear sections (purpose, why, prerequisites, returns), each sentence adds value, and it's front-loaded with the core purpose. There's no redundant or wasted text, making it efficient and easy to parse.

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 the tool's complexity (monitoring with prerequisites), no annotations, and an output schema (implied by 'Has output schema: true'), the description is complete. It explains the purpose, usage context, prerequisites, and return format, covering all necessary aspects without needing to detail outputs that the schema handles.

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?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, but it does provide context about the monitoring scope ('across the cluster') and prerequisites, which adds value beyond the empty 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 description clearly states the tool's purpose with specific verbs ('monitor GPU usage and health') and resources ('across the cluster'), distinguishing it from siblings like 'check_gpu_health' (which likely focuses on health only) and 'inspect_gpu_pod' (which targets specific pods). The 'Why' section reinforces this by explaining the monitoring goals.

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 'Prerequisites' section provides clear context for when to use this tool (requires NVIDIA GPU Operator), and the purpose implies it's for monitoring rather than diagnostics or inspection. However, it doesn't explicitly state when not to use it or name alternatives among siblings, such as when to choose 'check_gpu_health' instead.

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