$0.007 — Top 20 Hyperliquid leaderboard wallet aggregate positioning. Returns long/short notional split, average leverage, and directional bias (BULLISH/NEUTRAL/BEARISH).
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "whale_sentiment"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does useful work by disclosing the upstream data source (Hyperliquid leaderboard top 20), the exact output fields, and the BULLISH/NEUTRAL/BEARISH value domain. It also surfaces a $0.007 cost, which is genuinely useful behavior for a paid call. It stops short of stating how fresh the snapshot is, the aggregation window, or whether results are cached.
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?
Two sentences, zero filler: cost first, then scope, then return fields, with the bias enumeration last where it is easiest to scan. Every clause carries information an agent would otherwise have to infer.
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, so the description must explain returns, and it does so adequately by listing the three components and the bias categories. The notable gap is the time dimension: nothing says over what window the aggregate positioning is computed or how current the snapshot is, which matters for a sentiment signal.
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 takes zero parameters, so there is no parameter meaning to add; the baseline for a parameterless tool is 4. Nothing in the description misrepresents the input surface.
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 names a specific data source and scope: 'Top 20 Hyperliquid leaderboard wallet aggregate positioning,' and enumerates the outputs (long/short notional split, average leverage, directional bias). That is far more specific than the name alone, though it does not explicitly differentiate itself from plausible siblings like crowded_trade_detector or agent_conviction_score.
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 states what you get but not when to reach for it: no conditions, no exclusions, and no mention of when an alternative sibling would be preferable (e.g., per-trader vs. aggregate views). Usage is only weakly implied by the data being about whale positioning.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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