get_news_causality_breakdown

Item: get_news_causality_breakdown
Rating: 100
Author: Vouch

shallow

io.github.wnsod/oneqaz-trading-mcp · Verify this server

Purpose: Three-bucket news classification proving systematic discrimination between anticipated and surprise events. ANTICIPATED = scheduled + pre-move detected, SURPRISE_WITH_PRECURSOR = cascade anomaly (macro -> ETF -> stock) caught early, SURPRISE = pure unexpected. Triggers (casual questions too): "was that news already priced in?", "그 뉴스 예견된 거였어?", "how many surprise events this week?", "돌발 뉴스 비율 어때?", "did the market see it coming?". When to call: after get_news_leading_indicator_performance. Prerequisites: none. Next steps: market://{market_id}/external/causality for raw causality data. Caveats: window limited to recent days. Args: market_id: Market identifier days: Lookback window in days (default 7) Disclaimer: Information only, not investment advice.

100.0/100

1 trials · measured 8 days ago

get_news_causality_breakdown scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.wnsod/oneqaz-trading-mcp, measured 25 Aug 2026 under methodology v0.2.0. Every measured component scored 100.

Component breakdown

Component	Weight	Value
Reliability	35%	not applicable
Schema integrity	25%	100.0
Failure behaviour	15%	not applicable
Latency	15%	not applicable
Concurrency	10%	not applicable

Tool details

Transport: remote + stdio
Credential class: open
Category: Finance & compliance
Input schema: not declared
Output schema: not declared
Side-effect classification: unclassified

Score history

Day	Score	Tier	Methodology
2026-08-25	100.0	shallow	v0.2.0

Probe evidence

Probe	Outcomes
schema_integrity	pass: 1

Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.

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