hpc_gpu_models

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

With no annotations, the description carries the full burden. It reveals that the data is 'Live' (real-time), and it warns about the queue mixture (gpua100 mixes 40GB and 80GB nodes with no selector to force 80GB). This goes beyond the schema to disclose operational context. It does not mention idempotency or error behavior, but for a simple read tool, the key traits are covered.

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 extremely concise: a single sentence stating purpose and a usage note, followed by the parameter definition. Every sentence adds value (purpose, context, parameter). No fluff. It is front-loaded with the main purpose.

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 the tool's simplicity (one parameter, no nested objects, existence of output schema), the description covers all needed information: what it does, what it returns (models and availability), the parameter, and a usage hint. The output schema handles return value details, so this is complete for an agent to use correctly.

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 does so by defining the single parameter: 'queue: GPU queue name (e.g. "gpua100")'. This provides a clear description, type, and example, adding full meaning beyond the bare schema. For a one-parameter tool, this is excellent coverage.

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 states the tool provides 'Live GPU models and availability in a queue,' which clearly indicates it retrieves GPU model information for a given queue. The noun form is acceptable given the tool name, and it distinguishes from siblings like hpc_gpu_nodes by focusing on models rather than node details. However, it lacks an explicit action verb like 'get' or 'list,' so it's not maximally explicit.

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 advises 'Use before A100 jobs' and explains a specific scenario about queue mixing, providing context for when to use the tool. However, it does not explicitly mention alternatives or when not to use this tool. Sibling tools like hpc_gpu_nodes exist but are not compared, so guidance is implied rather than explicit.

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