Skip to main content
Glama

AgentFeed

by io.github.seekdaseek

get_cascade_forecast

FORWARD-LOOKING liquidation forecast, not a description of what already happened. Returns the probability that a symbol will liquidate more in the NEXT 15 minutes than its own 90th-percentile 15-minute window. Calibrated on a 28-day tape of 1.4M Bybit liquidations across 799 symbols, which cannot be reconstructed by anyone starting today because no exchange publishes liquidation history. Every answer carries the exact question, the threshold in USD, the window it read, the number of historical occurrences behind the number, and instructions for settling it yourself from the public feed. When a state has too little history the tool DECLINES rather than guessing, and says why. Costs $0.02 USDC per call (x402, Solana mainnet).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNoSOL, BTC, ETH or any USDT perp e.g. SXTUSDT (default SOL)
symbolsNocomma separated for a batch, max 20, e.g. SOL,BTC,ETH

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It explains the calibration source, why the data cannot be reconstructed, what each answer includes, the decline-when-uncertain behavior, and the exact cost and payment rail. This is exemplary transparency.

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 first sentence front-loads the core purpose, and every subsequent sentence adds a distinct operational fact: definition, calibration provenance, response contents, failure behavior, and cost. There is no filler or repetition.

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

Completeness5/5

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

There is no output schema, so the description must convey what the caller gets. It does: probability, threshold, window, historical occurrences, settling instructions, and decline behavior. Combined with fully documented optional parameters, nothing essential is missing for correct invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents symbol, symbols, defaults, and batch limits. The description adds no parameter-level detail beyond what the schema provides, so the baseline of 3 is appropriate.

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 states a specific verb and resource: it returns a forward-looking probability of liquidation in the next 15 minutes exceeding a symbol's own 90th-percentile window. It explicitly disclaims being a historical description, which differentiates it from history-oriented siblings. This is clear and distinct.

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 establishes when to use it: for forward-looking liquidation forecasts rather than historical facts, and notes that it declines when history is insufficient. It does not explicitly name alternatives like get_cascade_forecast_free or get_cascade_history, but the context strongly implies the intended use case.

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

All AgentFeed toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation3/5

Many tools are tightly scoped and cross-referenced, but the set contains overlapping families: liquidation tools (alert/scan/history/stats/leaders/recent/heatmap) and redundant snapshots like get_market_snapshot vs get_trade_context, get_last_liquidation vs get_recent_liquidations, and get_cascade_forecast_free vs get_cascade_forecast. Agents will need to read descriptions carefully to avoid misselection.

Naming Consistency4/5

All tool names follow a consistent get_<domain>_<detail> snake_case pattern, which makes the API predictable. The only real deviations are the bare 'pricing' tool and the 'free' suffix on taster variants.

Tool Count1/5

At 52 tools, this far exceeds the 3-15 well-scoped range and crosses the 50-tool extreme threshold. The count is inflated by numerous paid/free taster pairs and many overlapping liquidation variants.

Completeness4/5

The surface covers prices, funding, open interest, orderbooks, liquidations, wallet/token data, Solana network health, DeFi TVL, and stablecoin flows—broad coverage for a crypto data feed. Gaps like historical OHLC/price candles, a machine-readable symbol list, and pagination endpoints are workable around but would round it out.

Resources