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

With no annotations, the description carries the full burden and does well: it discloses that the recommendation includes cheapest platform and price, estimates total cost using an 8-hour assumption, and charges $0.05 USDC per call. It does not cover data freshness, failure behavior for unrecognized tasks, or availability constraints, but the core behavioral traits are disclosed.

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 purpose is front-loaded and the description is generally efficient, with task examples, coverage, return fields, and billing covered in a compact block. The repeated example framing and marketing sentence add slight redundancy but do not obscure the core information.

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 recommendation tool with no output schema and no annotations, the description supplies the key return fields, billing model, task coverage, and input examples. It is nearly complete for an agent to call correctly, with minor gaps around response structure details and handling of unsupported tasks.

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 coverage is 0%, so the description must compensate for the single 'task' parameter, and it does: it gives Chinese and English examples, lists eight task categories, and shows a sample call. It still leaves the exact accepted vocabulary open-ended rather than defining validation or fallback behavior.

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 a specific verb and resource: recommend the cheapest GPU rental plan for a given task, returning card type, platform, hourly price, and estimated total cost. It also distinguishes itself from sibling pricing tools by framing the capability as task-based quoting ('按任务问价'), which is not what gpu_price or gpu_history do.

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?

It gives clear usage context with example task inputs in both Chinese and English and lists eight supported task categories, making it easy to know when this tool applies. It does not explicitly name when to use gpu_price instead or when not to use this tool, so it stops short of full alternative routing.

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