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?

Beyond the `readOnlyHint: true` annotation, the description provides crucial behavioral context: the data source ('largest accounts by equity on Hyperliquid's public leaderboard'), the update frequency ('scanned every five minutes'), and the output structure ('levels are their own liquidation prices bucketed around the mark').

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 tool utilizes its descriptive space entirely efficiently. Every sentence serves to scope the data, define its source, or describe the expected output format without unnecessary filler.

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?

Despite lacking an output schema, the description provides all necessary context for an agent: what the data represents (liquidation price map), how fresh it is (5-minute scan), and how the resulting levels are categorized, making it a complete tool definition for an 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?

Given 100% schema description coverage, the baseline is high. The description reinforces the context of the parameters by linking 'hours' to an 'hourly archive of level totals' and the 'coin' to 'Hyperliquid names,' adding valuable operational meaning.

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 articulates the tool's specific function: mapping Hyperliquid whale tracked positions to liquidation prices. It provides multiple examples of use cases (notional at each price, comparison to LiqMap) which immediately define its purpose.

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 acts as a precise dispatcher, listing exact user intent phrases that necessitate this tool call. By providing specific data characteristics (tracked accounts, equity leader board) it implicitly defines its scope compared to broader market analysis tools.

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