gpu_metrics

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

The description discloses a key behavioral trait: reliance on nvidia-smi, implying unavailability on non-NVIDIA systems. With no annotations provided, this is valuable transparency. However, it does not clarify the exact behavior when GPU info is not available (e.g., returns null vs raise error).

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, concise sentence that front-loads the core purpose ('Get GPU information') and effectively communicates the key condition. No wasted words.

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 zero-parameter tool with an output schema, the description adequately conveys the main purpose and an important caveat (nvidia-smi dependency). It could be slightly more complete by stating the outcome when GPU info is unavailable, but overall it is sufficiently informative.

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 no parameters, and the input schema is fully documented (100% coverage). The description does not need to add parameter meaning, so the baseline of 4 applies.

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 gets GPU information, specifies it is availability-dependent (if available), and identifies the source (nvidia-smi). This distinguishes it from sibling tools like cpu_metrics or memory_metrics.

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 description implicitly indicates that this tool is for GPU-specific metrics, and sibling names suggest alternative tools for other metrics. However, it lacks explicit guidance on when to use this tool versus alternatives, such as conditions under which GPU info might be unavailable.

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