inspect_gpu_pod

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

With no annotations, the description carries full burden and discloses key behaviors: it runs a command inside a pod (implies execution capability), returns real-time output, and is specifically for GPU-enabled pods. It doesn't mention permissions, rate limits, or side effects, but covers core operational context adequately.

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?

Well-structured with clear sections (description, Why, Args, Returns), each sentence adds value. No redundant information; front-loaded with the core action, followed by rationale and details.

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?

Given 2 parameters, no annotations, and an output schema (implied by 'Returns'), the description is complete: it explains purpose, usage, parameters, and output. For a diagnostic tool with straightforward inputs/output, no critical 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?

Schema description coverage is 0%, so the description must compensate. It lists both parameters ('namespace', 'pod_name') under 'Args' with brief explanations, adding meaning beyond the bare schema. However, it doesn't specify format constraints or examples (e.g., namespace naming rules).

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 specific action ('Run nvidia-smi inside a GPU-enabled pod') and resource ('GPU-enabled pod'), with distinct purpose from siblings like 'get_gpu_utilization' (monitoring) or 'get_pod_diagnostics' (general diagnostics). It explicitly mentions viewing 'real-time process and memory details'.

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 'Why' section provides explicit guidance on when to use this tool: for debugging OOM, verifying GPU allocation, and checking processes. It implicitly distinguishes from siblings by focusing on real-time GPU process inspection rather than health checks, logs, or utilization metrics.

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