Hyperliquid tracked positions: the liquidation price map of the largest accounts per coin, against the LiqMap model

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

Annotations already declare this as a read-only, open-world lookup. The description adds meaningful provenance and behavior: the universe is the largest accounts by equity on Hyperliquid's public leaderboard, scanned every five minutes, with levels being the accounts' own liquidation prices bucketed around the mark. It does not describe return shape or caveats.

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 front-loaded with usage triggers, followed by source/behavior details and then the parameter note. It is dense but each sentence serves a purpose, with minimal waste.

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?

There is no output schema, and the description compensates by explaining the data universe, refresh cadence, bucket logic, and archive behavior. It remains somewhat incomplete on the exact return fields, but it gives enough context for correct invocation.

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 adds some semantic context for hours by stating it returns the hourly archive of level totals, though it adds little beyond the schema for the coin parameter.

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 resource: the liquidation price map of the largest tracked Hyperliquid accounts, per coin. It distinguishes itself from get_liqmap by framing the data as account-level levels compared against the LiqMap model, and from general whale tools by specifying tracked leaderboard accounts.

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 user-question triggers: liquidation levels for Hyperliquid whales, tracked notional at each price, large-account positioning, and comparison with the LiqMap model. It does not explicitly name a when-not-to-use case or a direct alternative tool, so it falls short of the top score.

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