get_gpu_usage

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

Annotations already indicate read-only and non-destructive behavior. The description adds valuable context beyond annotations: the dependency on nvidia-ml-py and NVIDIA hardware, and the fact that it returns an inline grouped bar chart. This gives the agent a better sense of prerequisites and output format without contradicting annotations.

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 sentence that front-loads the core purpose, then adds dependency and output details. It is concise with no redundant information, making it easy to parse quickly.

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 read-only tool with no parameters and no output schema, the description fully covers the necessary context: what is returned, the hardware/software requirements, and the format (chart). An agent can confidently invoke this tool based on this description alone.

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 zero parameters, so the schema is empty and description coverage is 100%. The baseline for no parameters is 4, and the description adds meaning by specifying what metrics are returned (load, VRAM, temperature), which is relevant context even without parameters.

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 what the tool does: returns GPU load, VRAM usage, and temperature. It uses a specific verb and resource, and the mention of GPU-specific metrics distinguishes it from sibling system monitoring tools like get_cpu_usage and get_ram_usage.

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 implies usage context by naming GPU metrics, making it clear this is for GPU monitoring. However, it does not explicitly state when to use this tool instead of alternatives or mention any exclusions. The dependency note (requires nvidia-ml-py) gives a prerequisite but not explicit guidance on tool selection.

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