Liquidation map of large accounts

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

Annotations already declare readOnly/idempotent/non-destructive, so the description adds real behavioral context: the data comes from an undocumented file the web app loads rather than an official API, sampling is capped at 100 accounts, and longs bucket below the mark while shorts bucket above. It also gives the reconciliation invariant (in range + outside + no liquidation price = total) so results can be sanity-checked. It omits failure modes and latency, keeping it at 4.

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?

Three sentences, front-loaded with the core purpose and no filler. The dense 'in range + outside + no liquidation price = total' phrasing takes a re-read, and the data-source caveat sits slightly awkwardly at the end, but nothing is wasted.

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 derived analytics call with no output schema, the description covers provenance, the sampling cap, bucket geometry, and a totals invariant, which is enough to call and interpret it. It does not describe the response structure in detail or what happens for an unknown coin, leaving a small gap.

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%, so the baseline is 3; the description goes further by clarifying that 'top' is a sample of the ranking, that 'rank' means 30-day volume or account value, and that the buckets run around the mark with longs below and shorts above. That adds interpretation the schema alone does not convey.

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 outcome: it ranks the Hyperliquid leaderboard by 30-day volume or account value, reads public positions of the top accounts, and buckets notional by liquidation price around the mark. That is a concrete verb-plus-resource definition an agent can match to a request. It does not name a sibling (e.g. liq_price) to distinguish itself, so it stops short of 5.

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?

Usage is implied rather than stated: the description makes clear you'd call it to see liquidation clustering for one coin, and the 'at most 100' cap is disclosed. But there is no explicit when-not guidance and no routing to alternatives such as liq_price or hl_positions for single-account lookups.

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