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APEX Runner Crypto Signals

crowded_trade_detector

$0.007 — Call this before entering a popular directional trade so you don't become the exit liquidity — scores crowding 0-100 per coin from Hyperliquid's top-20 leaderboard positions (long/short split, leverage) and returns a contrarian signal plus uncrowded opportunities.

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": "crowded_trade_detector"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does most of the work: it discloses the upstream data source (top-20 leaderboard positions), the inputs it derives from (long/short split, leverage), the output form (0-100 score, contrarian signal, opportunity list), and the price. It does not address refresh cadence, latency, or coin coverage, which are the remaining unknowns for a live-data scoring tool.

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

Conciseness4/5

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

A single dense sentence that front-loads the price, then the trigger, then the mechanism and outputs — nothing is redundant. The em-dash construction is slightly heavy but every clause adds information an agent needs.

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 parameterless tool with no output schema, the description must describe return values itself, and it does: a 0-100 crowding score, a contrarian signal, and a list of uncrowded opportunities. Missing only the universe of coins covered and how fresh the leaderboard data is, which leaves a small gap for a data-sourced tool.

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 schema has zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The description implies the caller supplies no filters, which is consistent with the empty schema.

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

Purpose5/5

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

States a specific verb and resource (scores crowding 0-100 per coin), names the data source (Hyperliquid's top-20 leaderboard positions with long/short split and leverage), and lists both halves of the output (contrarian signal plus uncrowded opportunities). No sibling does anything similar, so no differentiation text is needed.

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

Usage Guidelines4/5

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

Gives an explicit trigger condition: call it before entering a popular directional trade to avoid becoming exit liquidity. That is clear when-to-use guidance grounded in a concrete motivation. It stops short of stating when-not-to-use or naming an alternative tool for the opposite question.

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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