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Volume Breakout Scanner

volume_breakout_scanner
Read-only

Detect coins with volume breakout + price breakout.

Args:
    exchange: Exchange name like KUCOIN, BINANCE, BYBIT, MEXC, etc.
    timeframe: One of 5m, 15m, 1h, 4h, 1D, 1W, 1M
    volume_multiplier: How many times the volume should be above normal level (default 2.0)
    price_change_min: Minimum price change percentage (default 3.0)
    limit: Number of rows to return (max 50)

Returns ``list[dict]`` on success, or an error envelope on total upstream
failure (``{"error": {"code": "ALL_BATCHES_FAILED", ...}}``). The empty
list now strictly means "no matches today"; rate-limit cliffs surface
explicitly.

Input Schema

NameRequiredDescriptionDefault
limitNo
exchangeNoKUCOIN
timeframeNo15m
price_change_minNo
volume_multiplierNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful semantics beyond that: the empty list strictly means 'no matches today,' total upstream failure returns an error envelope (ALL_BATCHES_FAILED), and rate-limit cliffs surface explicitly rather than silently.

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?

Front-loaded with the purpose, followed by a clean Args list and a returns note; every element maps to something an agent needs. The trailing sentence about rate-limit cliffs is slightly verbose but earns its place by clarifying error behavior.

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?

With an output schema present, the description needn't detail return shapes, yet it usefully clarifies the empty-list-vs-error distinction. Parameter documentation is complete enough to invoke correctly; the remaining gap is the absence of any usage guidance relative to the many sibling scanners.

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?

Schema description coverage is 0%, so the description carries the full parameter burden and largely does: it explains exchange examples, enumerates the valid timeframes, defines volume_multiplier as a multiple of normal volume, defines price_change_min as a percentage, and caps limit at 50. It omits the limit default (25) and gives no guidance on tuning thresholds, keeping it short of a 5.

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 opens with a specific verb and resource: 'Detect coins with volume breakout + price breakout,' which tells an agent exactly what the tool produces. It does not, however, differentiate itself from close siblings like smart_volume_scanner or volume_confirmation_analysis, so an agent cannot resolve the overlap from the text alone.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool, when not to, or which sibling to prefer for adjacent volume/breakout analysis. The Args block documents inputs but gives no usage context or prerequisites, leaving routing entirely 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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