get_gpu_info

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

With no annotations provided, the description carries the full burden and excels. It discloses the read-only nature, the backend tool fallback order (nvidia-smi, rocm-smi, intel_gpu_top), fatal failure only when no GPU tool exists, and the critical Intel fallback limitation (presence only, not metrics).

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?

Four short sentences, each earning its place: purpose, safety/backend, failure behavior, fallback limitation, and usage guidance. Front-loaded with the main functionality; zero redundancy or fluff.

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 (which handles return-value details), the description covers everything else: what it returns, safety, backend selection, failure mode, fallback caveat, and use case. No meaningful gaps remain.

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 per rubric baseline is 4. The description correctly omits parameter details since there are none to explain; the empty schema requires no compensation.

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 opens with a specific verb and resource: 'Returns GPU usage, memory, temperature, and power draw.' This precisely states what the tool does and clearly differentiates it from siblings like get_cpu_info and get_memory_info.

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 closing sentence, 'Use for GPU workload monitoring,' provides explicit when-to-use context. However, it does not name alternatives or state when not to use this tool, so it stops short of full exclusionary guidance.

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