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LiquidVision — Crypto Derivatives Data

get_liquidation_cascades

Liquidation cascades as discrete events rather than a raw feed.

Each event: coin, which side broke, when it started, how long it ran, total
and peak-minute USD, how many multiples of that coin's own normal minute it
was, the move in cross-venue median mark price during it, and what price did
in the hour after. Also returns market_wide: minutes when three or more
unrelated coins cascaded together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNo
limitNo

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 provided, the description carries the full behavioral burden. It transparently discloses that the tool aggregates raw data into discrete events and explains what each event contains, including the market_wide composite. However, it does not disclose pagination, result ordering, the meaning of the 'normal minute' baseline, or any rate/auth considerations. It discloses the core transformation but not operational details.

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?

The description front-loads the core purpose in the first sentence, then uses a tight list to enumerate return fields, and closes with the market_wide special case. Every sentence earns its place and the layout is logical. Slightly verbose in the field enumeration, but well-organized for an agent to parse.

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

Completeness3/5

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

Because there is no output schema, the description correctly takes on the burden of explaining return values, and it does so thoroughly. However, the two parameters are completely undocumented in both schema and description, and with no annotations the operational context (limits, defaults, ordering) is left for the agent to guess. Reasonably complete on the output side, incomplete on the input side.

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

Parameters2/5

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

Schema description coverage is 0% and the description never mentions the 'hours' or 'limit' parameters at all. While their purpose (time range and result cap) is inferable from their names, the description provides zero guidance on semantics, valid ranges, or defaults beyond what the schema's default values show. With low coverage, the description was obligated to compensate and did not.

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?

The description uses a specific verb-resource pair ('Liquidation cascades as discrete events') and explicitly contrasts with 'a raw feed,' which is a sibling tool (get_liquidations). It enumerates the exact fields returned (coin, side, duration, USD totals, price moves), making the tool's function unmistakable and distinguishing it from its 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?

The phrase 'rather than a raw feed' implies the tool is the aggregated/event-level alternative to a raw liquidation feed, but it never names the sibling (get_liquidations) or states explicit conditions for when to choose this over the raw feed or the heatmap/map siblings. Usage context is implied, not explicit, and no exclusions are given.

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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