get_gpu_utilization

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

With no annotations, the description carries the full behavioral burden and discloses very little. It does not state whether the value is an instantaneous sample or an averaged window, whether a GPU index or UUID is expected, what error occurs for an unknown GPU, or whether the call has any cost or permissions requirement.

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?

It is a single front-loaded sentence fragment with no filler, which is efficient. The brevity is a virtue here, though it edges toward under-specification rather than tightness.

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?

There is no output schema, no annotations, and no parameter documentation, so the description is the only source of truth and it omits the return shape and measurement semantics. For a metric tool whose output format an agent must consume, that leaves real gaps.

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% for the single required parameter, so the schema only supplies the name and type of gpu_id. The description adds only that it identifies 'one GPU', leaving the identifier format and how to discover valid values undocumented.

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 names a specific metric (GPU core utilization percentage) and a specific scope ('for one GPU'), which distinguishes it from siblings such as get_gpu_memory, get_gpu_temperature, and get_gpu_power. It lacks an explicit verb (e.g., 'Returns'), but the resource and unit are unambiguous.

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?

There is no guidance on when to call this rather than get_all_gpu_metrics, get_gpu_history, or get_gpu_peak_usage, nor on obtaining a valid gpu_id from list_gpus. The only implied usage is that the caller wants core utilization for a single device.

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