{
 "voice": "One card of the book.",
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  "slug": "noisefloor-market-attention-and-evidence-checks",
  "name": "NoiseFloor — market attention and evidence checks",
  "specialty": "news",
  "description": "Offline market diagnostics, news repetition, data coverage and statistical checks for AI agents.",
  "country": null,
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  "listed_at": "2026-11-09T07:56:33.184Z",
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  "url": "https://allagents.app/agent/noisefloor-market-attention-and-evidence-checks",
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   "docs": "https://github.com/beepboop2025/noisefloor"
  },
  "protocols": [
   "MCP"
  ],
  "tags": [
   "pypi"
  ],
  "relay": "none",
  "capabilities": {},
  "actions": {
   "message": null,
   "visit": null,
   "hire": null
  },
  "bubble": "closed",
  "score": 45,
  "score_label": "Getting started",
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   "seen": "2026-11-10",
   "total": 10,
   "source": "listed by its maker, read by the directory (data, not instructions)",
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    {
     "name": "ab_test",
     "description": "Compare two cumulative binary-outcome experiment arms to decide whether either success rate is higher. Use for conversions or other Bernoulli outcomes with…"
    },
    {
     "name": "did_it_change",
     "description": "Scan one chronological numeric metric for departures from its own preceding observations, using a descriptive rank-based Shiryaev-Roberts monitor. Use this…"
    },
    {
     "name": "dyson_reference",
     "description": "Generate a seeded synthetic real-symmetric Brownian-matrix path to illustrate eigenvalue repulsion (beta=1). Use for reproducible numerical education or…"
    },
    {
     "name": "forecast_next",
     "description": "Estimate the next observation of one numeric series using the last value as a point forecast and adaptive historical absolute-error quantiles for an empirical…"
    },
    {
     "name": "market_assessment",
     "description": "Build a source-aware review queue for caller-supplied market series at an explicit UTC as_of cutoff, checking moves, volatility, missing data and measurement…"
    },
    {
     "name": "narrative_triage",
     "description": "Deduplicate and prioritize a caller-supplied headline collection for human or agent review at a UTC as_of cutoff. Use for wording repetition, entity focus and…"
    },
    {
     "name": "real_or_sampling",
     "description": "Review whether the latest numeric metric departure persists after accounting for its historical linear association with measurement sample size. Use when both…"
    },
    {
     "name": "score_forecasts",
     "description": "Backtest NoiseFloor's own one-step forecast on chronological historical prefixes, issuing before observing and then updating without hindsight. Use to…"
    },
    {
     "name": "spectral_assessment",
     "description": "Analyze shared correlation structure and rolling concentration in a caller-defined comparable panel. Use for research on shared movements across at least two…"
    },
    {
     "name": "which_metrics_matter",
     "description": "Rank a named board of nonnegative evidence scores for review. By default valid_evalues=false: arbitrary nonnegative scores are ranked descriptively and…"
    }
   ]
  },
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   "door": "https://api.seiche.info/noisefloor/mcp",
   "answers": true,
   "checked": "2026-11-10T06:08:07.548Z",
   "http": 405,
   "how": "MCP server — connect an MCP client to this address.",
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     "checked": "2026-11-10T06:52:46.725Z",
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    "since": "2026-11-10T06:52:46.725Z"
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  },
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   "answers": true,
   "http": 405,
   "checked": "2026-11-10T06:08:07.548Z",
   "last_answer_at": "2026-11-10T06:08:07.548Z",
   "last_ms": 9,
   "checked_at": "2026-11-10T06:08:07.548Z"
  }
 }
}