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

Annotations already declare readOnly, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely new behavior: the $0.02 USDC-on-Base cost, the plan-key alternative, and the cluster-collapsing ranking logic. No return-format detail, but the cost and methodology disclosures are real value beyond the annotations.

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?

Front-loads the ranking methodology, then differentiator, window, and cost in tight succession. Slightly dense but every clause carries information; the payment parenthetical is a minor appendage.

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 one-parameter tool with no output schema, the description covers purpose, scope, window bounds, and the important payment model. It could sketch the returned fields, but the essentials for correct invocation are present.

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 100% and the single parameter is fully documented in the schema ('lookback window in hours, 1-48 (default 6)'). The description repeats that same window/default information without adding format or behavior detail, so the baseline 3 applies.

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?

States a specific resource (Robinhood Chain tokens) and the exact ranking methodology: caller-accuracy weight, then independent voices, then mention count, with cluster collapsing. The phrase 'Attention QUALITY, not raw hype counts' distinguishes it conceptually from hype-based siblings, though it never names an alternative tool outright.

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 framing 'Attention QUALITY, not raw hype counts' implies the intended use case, but there is no explicit when-to-use or when-not guidance and no sibling named (e.g. robinx_leaderboard, robinx_mentions). Usage must be inferred from the ranking description.

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