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crypto-quant-signal-mcp

by AlgoVaultLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

scan_trade_calls

Read-only

Scan top crypto perpetual futures by open interest to get ranked BUY, SELL, or HOLD trade calls with confidence and market regime for whole-market breadth.

Instructions

Returns ranked BUY SELL HOLD trade calls across the top crypto perpetual futures by open interest — one scan for whole-market coverage, each with confidence and market regime. Use this for breadth; use get_trade_call for per-coin depth and reasoning. Read-only: reads live exchange APIs, places no orders.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNNoHow many top perps by open interest to scan, 1 to 100 (default 20).
limitNoMax ranked calls to return, 1 to 100 (default 10). Non-HOLD ranked first.
rankByNoUniverse lens: oi (default) volume gainers losers movers funding_positive funding_negative volatility oi_change (aliases vol gain lose move pfr nfr atr oid). funding_*/volatility/oi_change rank among the most-liquid perps; oi_change = real 24h open-interest %Δ.oi
oiBasisNoOI-delta basis for rankBy=oi_change: notional (default, USD) or contracts (base-coin, price-independent). Ignored by other lenses.notional
exchangeNoCrypto venue (default Binance), e.g. Binance Bybit OKX Bitget Hyperliquid.BINANCE
timeframeNoCandle timeframe, 1m to 1d for the scan. Default 15m intraday.15m
includeHoldsNoInclude HOLD calls after non-HOLD (default false).
minConfidenceNoOptional confidence floor, 0 to 100, applied to non-HOLD trade calls.
oiChangeWindowNoOI-delta window for rankBy=oi_change: 1h, 4h, or 24h (default 24h). Ignored by other lenses.24h
minLiquidityUsdNoOptional USD liquidity floor applied to the scan universe: notional open interest, or 24h volume on venues that expose no bulk OI. Omitted means no floor.
includeReasoningNoEnrich each non-HOLD call with price, the top 2-3 drivers, and one-line reasoning (default false → bare verdict cells). HOLDs stay bare. Same per-call detail as get_trade_call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.30.0
    • changedInput schema / properties / exchange / enum
      Previous value: -[
-  "HL",
-  "BINANCE",
-  "BYBIT",
-  "OKX",
-  "BITGET",
-  "ASTER",
-  "BINGX",
-  "GATE",
-  "HTX",
-  "KUCOIN",
-  "MEXC",
-  "PHEMEX",
-  "WHITEBIT",
-  "BITMART",
-  "XT"
-]New value: +[
+  "HL",
+  "BINANCE",
+  "BYBIT",
+  "OKX",
+  "BITGET",
+  "ASTER",
+  "BINGX",
+  "GATE",
+  "HTX",
+  "KUCOIN",
+  "MEXC",
+  "PHEMEX",
+  "WHITEBIT",
+  "XT",
+  "WEEX"
+]
  2. Changed1 schema field changedv1.28.2
    • changedInput schema / properties / exchange / description
      Previous value: -"Venue: BINANCE (default) HL BYBIT OKX BITGET."New value: +"Crypto venue (default Binance), e.g. Binance Bybit OKX Bitget Hyperliquid."
  3. Changed1 schema field changedv1.26.0
    • changedInput schema / properties / includeHolds / description
      Previous value: -"Include HOLD calls after non-HOLD (default false). HOLDs never cost quota."New value: +"Include HOLD calls after non-HOLD (default false)."
  4. First observedv1.25.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=true and destructiveHint=false. The description adds context beyond that by stating it 'reads live exchange APIs' and 'places no orders,' and it discloses output characteristics: ranked calls, confidence, and market regime. There is no contradiction with 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?

Three sentences carry all essential information: what it returns, when to use it, and its safety profile. The main purpose is front-loaded, usage guidance comes second, and safety is concise. No filler or redundant restating of the name.

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?

For an 11-parameter, read-only market scanner with no output schema, the description covers purpose, breadth-versus-depth routing, safety, and the shape of the result. The remaining operational details (parameter meanings, defaults, constraints) are fully covered by the rich input schema.

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%, and the schema already thoroughly documents every parameter with defaults, ranges, enums, and condition-specific meanings. The description adds high-level context about whole-market scanning but does not need to repeat parameter details. Baseline 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 opens with a specific verb and resource: 'Returns ranked BUY SELL HOLD trade calls across the top crypto perpetual futures by open interest.' It clearly distinguishes itself from the sibling get_trade_call by framing this as whole-market breadth versus per-coin depth, so an agent can select it without inspecting schemas.

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

Usage Guidelines5/5

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

The description explicitly says 'Use this for breadth; use get_trade_call for per-coin depth and reasoning.' This provides a direct when-to-use rule and names the alternative, making the selection decision unambiguous.

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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