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akari-x402

akari_hl_roundtrips

HyperLiquid trade tape dataset - every closed round trip from this desk: coin, side, entry, exit, size, notional, hold time, fee and net PnL, exchange-settled. 300+ rows including the losers, which is the part no other seller publishes. One payment, full JSON: backtest filters, train a model, or audit our claims. Free 3-row sample at /x402/dataset/preview.

Tool call template

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": "akari_hl_roundtrips"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose useful traits: 300+ rows, includes losing trades, exchange-settled, and 'one payment, full JSON', which signals a paid one-shot retrieval. It stops short of explaining payment mechanics, whether the call itself is metered, or any rate/response constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core dataset claim is front-loaded, but the copy is written as a sales pitch ('the part no other seller publishes') and spends words on marketing rather than operational detail. It is not bloated to the point of confusion, but several phrases do not help an agent invoke the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no output schema, the description does the necessary work of enumerating the returned fields and row count. The main gap is the absence of any note on how the paid retrieval is delivered, but overall it is complete enough to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a no-param tool applies. The field list describes output content rather than inputs, which is appropriate here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (HyperLiquid trade tape dataset of closed round trips) and enumerates the returned fields (coin, side, entry, exit, size, notional, hold time, fee, net PnL). It lacks an explicit verb like 'fetch' or 'returns', but an agent can still tell this is a dataset-retrieval tool distinct from the analysis-oriented siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It offers usage contexts ('backtest filters, train a model, or audit our claims') and points to a free 3-row preview endpoint, which implies a try-before-buy path. However, it gives no when-not guidance and does not contrast with any sibling tool, so selection against alternatives is left to inference.

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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