Skip to main content
Glama

Auto-detected liquidation cascades (forensic case file)

get_liquidation_cascades
Read-only

See what caused a recent crypto crash or flush: auto-detected liquidation cascades with timing, total notional flushed, long/short split, leading coins, and BTC's move from a real tape.

Instructions

Call this when the user asks what caused a recent crash or flush, about liquidation cascades, or who got liquidated. Returns auto-detected cascade incidents: when, total notional flushed, long/short split, which coins led, and BTC's move during the window. Totals are an honestly-labeled lower bound from a real liquidation tape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint and openWorldHint, and the description adds meaningful behavioral context beyond them: results are auto-detected, totals are an honestly-labeled lower bound, and the data comes from a real liquidation tape. This manages expectations about data completeness and provenance without contradicting annotations.

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

Conciseness5/5

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

The description is compact and front-loaded: the trigger condition appears first, followed by the output contents, then the important caveat about lower-bound totals. Every sentence earns its place and there is no redundancy or filler.

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 no-argument, read-only tool with no output schema, the description covers when to call it, what data it returns, and a key limitation. It could go slightly further on output formats or explicitly distinguish itself from get_liquidations for raw per-liquidation data, but it is sufficient for selecting and invoking the tool 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 has zero parameters and an empty input schema, so there is no parameter semantics to document. Per the baseline for zero-parameter tools, a 4 is appropriate; the description's focus on output fields is correct and does not need to compensate for missing parameter docs.

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 clearly names the resource (auto-detected liquidation cascades), specifies the action (returns cascade incidents), and enumerates the returned fields (when, total notional flushed, long/short split, leading coins, BTC's move). This differentiates it from siblings like get_liquidations by emphasizing cascade incidents and the forensic case-file framing.

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?

The description opens with explicit trigger conditions: 'Call this when the user asks what caused a recent crash or flush, about liquidation cascades, or who got liquidated.' This gives clear context for when to use the tool, but it does not name alternatives or explicitly state when not to use it, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.