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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states that the tool is descriptive rather than a forecast, which is a critical trait for an agent to know it won't get predictions. It also lists the outputs (percentile, 24h trend, verdict), giving a clear picture of the result. It does not mention any limitations or additional caveats, but for a descriptive tool this is sufficient.

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 a single, densely informative sentence. It front-loads the context ('Rent-now-or-wait context'), then describes the output and nature. Every word earns its place; there is no fluff. It is appropriately concise for a simple tool.

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?

The description adequately conveys what the tool returns (percentile, trend, verdict) and its descriptive nature, so an agent knows what to expect. However, the lack of parameter guidance means the tool cannot be called correctly without additional inference, especially the distinction between gpu and gpu_model and the meaning of offer_class. Given the missing output schema and 0% parameter coverage, the description does not fully equip the agent.

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 schema covers 0% of parameters (no descriptions), and the description does not compensate by explaining gpu, gpu_model, or offer_class. The phrase 'one GPU model' hints at gpu_model, but gpu and offer_class are completely unexplained, including their distinction or valid values. This is a significant gap, making it hard for an agent to know how to populate the parameters correctly.

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's purpose: it situates the current best price for a GPU model within its trailing 7/30-day range, providing a percentile, 24h trend, and verdict. It explicitly says it is descriptive, not a forecast, which distinguishes it from predictive or raw historical tools like price_history. This is specific and unambiguous.

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 opens with 'Rent-now-or-wait context', which gives a clear indication of when to use it: for deciding whether to rent now or wait for a better price. It does not explicitly name alternatives or state conditions for not using it, but the context is strong enough for an agent to infer its scenario. Lack of explicit sibling routing keeps it from a 5.

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