get_gpu_metrics

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

No annotations provided, and the description does not disclose behavioral traits beyond stating it returns time-series data. It mentions the hours parameter with default and max, but lacks details on data source, update frequency, or any side effects. Adequate but not rich.

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?

Concise and well-structured: first sentence states the main action, then usage guidance, then parameter details. Every sentence adds value with minimal wordiness.

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 simple tool with one parameter and no output schema, the description is quite complete. It covers what it returns, when to use, and alternatives. A minor omission is the return format of the time series, but not critical.

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 schema has 0% description coverage, but the description adds 'Look-back window in hours (default 24, max 168)', providing context beyond the schema's type and default.

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 'Return hourly GPU utilisation time-series for the last N hours' with a specific verb (Return) and resource (GPU utilisation time-series). It clearly distinguishes from siblings by naming alternatives for cost and workload breakdown.

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?

Explicitly says when to use ('assess whether GPUs are idle, saturated, or trending toward failure') and provides alternatives: 'For cost implications... use get_cost_estimate. For a breakdown... use get_workload_breakdown.'

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