sample_gpu_usage

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

With no annotations, the description must disclose behavior. It states the tool uses nvidia-smi and optionally writes JSONL, but does not mention whether the operation is read-only, whether it blocks, or file writing behavior (overwrite/append). Missing details on prerequisites and side effects.

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?

Single sentence is concise and front-loaded with the core action. However, the structure could be improved by separating the sampling description from the optional JSONL part. 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?

No output schema, so the description should explain what is returned (e.g., sampled GPU metrics). It does not. Additionally, two of three parameters are undescribed, making the tool usage incomplete for an agent. Contextual completeness is low.

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 has 3 parameters (intervalSeconds, count, savePath) but 0% description coverage. The description only mentions the optional JSONL writing (savePath), leaving intervalSeconds and count unexplained. Even the tool name suggests sampling, but the description does not connect these parameters to the sampling process.

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?

Description clearly states it samples GPU utilization using nvidia-smi through Colab Terminal and optionally writes JSONL. The verb 'samples' and resource 'GPU utilization' are specific, and it distinguishes from sibling tools like check_gpu (check current state) and start_gpu_monitor (continuous monitoring).

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?

No explicit guidance on when to use this tool vs alternatives like check_gpu or start_gpu_monitor. The description implies it's for sampling with nvidia-smi, but does not state when sampling is appropriate or provide exclusions.

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