list_gpus

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states 'Read-only, no cost,' which is a key safety signal. It also indicates real-time availability ('right now') and the action to take with results, adding meaningful behavioral context beyond a simple list.

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 with a distinct purpose: main action, usage flow, safety indication, and selection heuristic. No redundancy or filler, and the most important information is front-loaded.

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 parameterless list tool with no output schema, the description covers all necessary context: what it lists, what data it provides, how to use the result (node_id), safety profile (read-only), and a practical usage guideline. This is complete for an agent to invoke and interpret the tool 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?

The tool has zero parameters and an empty schema, so there is no parameter-specific information to provide. The baseline of 4 for no parameters is appropriate; the description doesn't need to compensate for any schema gaps.

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 tool lists GPU nodes available to rent, enumerating specific fields (price, model, VRAM, CUDA compute capability, country). It distinguishes itself from sibling tools like list_rentals by focusing on available inventory rather than user's current rentals.

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 provides clear context that this is a discovery step before renting ('Rent by the returned node_id') and offers selection guidance for LLM work (compute_cap >= 8.9). However, it doesn't explicitly say when not to use it or contrast with alternatives, so it stops just short of a 5.

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