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

With no annotations provided, the description carries the full transparency burden. It discloses the calculation basis ('this week's median against last week and against the first week collected'), data-quality handling ('days excluded as marketplace glitches'), and a clear limitation ('never forecasts'). This is detailed, non-obvious behavioral context.

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 core metric is front-loaded, and each clause adds a distinct field or caveat. The two-sentence structure is dense but not bloated; the final sentence succinctly prevents misinterpretation. The long first sentence has comma-heavy complexity, so it is not perfectly clean.

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 report with no output schema, the description specifies the key fields (median, min/p75, offer counts, timestamp), the Rent Index's exact reference periods, the trend word, and excluded glitch days. No critical information for invoking or interpreting the tool is missing.

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 accepts zero parameters, so the description has no parameter semantics to explain. Per the rubric, 0 params earns a baseline of 4; the description appropriately focuses on the output fields and computation rather than inputs.

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 what the tool reports: 'Median verified on-demand rental price per GPU class' and enumerates the accompanying metrics. It lacks an imperative verb like 'returns' or 'lists', but the subject matter is unambiguous and distinct from siblings such as find_fit or list_hardware.

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?

There is no explicit guidance for when to use this tool versus sibling tools. The only usage-relevant statement is the negative constraint 'it never forecasts', which implies historical analysis rather than predictive use, but does not name alternatives or conditions.

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