NoiseFloor — market attention and evidence checks

Name: NoiseFloor — market attention and evidence checks
Author: beepboop2025

MCP serverMCP

Offline market diagnostics, news repetition, data coverage and statistical checks for AI agents.

by beepboop2025repository ↗homepage ↗v0.4.3MIT

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Evidence

Everything here was measured by our prober or read from a registry. Nothing is self-reported.

30-day availability61/61 checks ok

when	check	result	http	latency	detail
4 h ago	mcp initialize	ok	200	21 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
11 h ago	mcp initialize	ok	200	19 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
19 h ago	mcp initialize	ok	200	16 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
26 h ago	mcp initialize	ok	200	16 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
33 h ago	mcp initialize	ok	200	19 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
2 d ago	mcp initialize	ok	200	16 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
2 d ago	mcp initialize	ok	200	18 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
2 d ago	mcp initialize	ok	200	23 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
3 d ago	mcp initialize	ok	200	26 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools
3 d ago	mcp initialize	ok	200	24 ms	server noisefloor 0.4.3 · protocol 2025-06-18 · 10 tools

Tools observed via MCP handshake

ab_test — Compare two cumulative binary-outcome experiment arms to decide whether either success rate is higher. Use for conversions or other Bernoulli outcomes with cons
did_it_change — Scan one chronological numeric metric for departures from its own preceding observations, using a descriptive rank-based Shiryaev-Roberts monitor. Use this for
dyson_reference — Generate a seeded synthetic real-symmetric Brownian-matrix path to illustrate eigenvalue repulsion (beta=1). Use for reproducible numerical education or researc
forecast_next — 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 i
market_assessment — 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 co
narrative_triage — 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 p
real_or_sampling — Review whether the latest numeric metric departure persists after accounting for its historical linear association with measurement sample size. Use when both a
score_forecasts — Backtest NoiseFloor's own one-step forecast on chronological historical prefixes, issuing before observing and then updating without hindsight. Use to evaluate
spectral_assessment — Analyze shared correlation structure and rolling concentration in a caller-defined comparable panel. Use for research on shared movements across at least two se
which_metrics_matter — Rank a named board of nonnegative evidence scores for review. By default valid_evalues=false: arbitrary nonnegative scores are ranked descriptively and selected

Declared by sources

What the publisher says

Attributed to the source that supplied each field. Treated as claims, not facts.

No long description beyond the summary.

ab-testingagentsanomaly-detectionanytime-validcalibrationchange-detectionconfidence-sequenceconformal-predictiondata-qualityexperimentationfinancial-researchliquilensmarket-datamcpmonitoringnews-deduplicationpalimpsestseichesequential-testingstatisticsundertow

kind Official MCP Registry

name Official MCP Registry

license PyPI

repoUrl Official MCP Registry

summary Official MCP Registry

version Official MCP Registry

description Official MCP Registry

homepageUrl PyPI

publisherName Official MCP Registry

Provenance

Where this record came from

Every source is kept verbatim. Field changes are logged as events.

Official MCP Registryio.github.beepboop2025/noisefloor

first seen 2 mo ago · fetched 14 h agoopen ↗

PyPInoisefloor

first seen 2 mo ago · fetched 21 h agoopen ↗

Field history (12 recent events)

7 d ago · declared · homepageUrl → "https://beepboop2025.github.io/financial-evidence-skills/api/noisefloor/"
7 d ago · observed · packageFacts → {"name":"noisefloor","registry":"pypi","publishedAt":"2026-10-18T00:05:27.592436Z","latestVersion":"0.4.3"}
8 d ago · declared · version → "0.4.3"
16 d ago · observed · packageFacts → {"name":"noisefloor","registry":"pypi","publishedAt":"2026-10-09T13:31:14.953133Z","latestVersion":"0.4.0"}
17 d ago · declared · description → "Offline market diagnostics, news repetition, data coverage and statistical checks for AI agents."
17 d ago · declared · version → "0.4.0"
17 d ago · declared · summary → "Offline market diagnostics, news repetition, data coverage and statistical checks for AI agents."
17 d ago · declared · name → "NoiseFloor — market attention and evidence checks"
2 mo ago · observed · packageFacts → {"name":"noisefloor","registry":"pypi","publishedAt":"2026-08-02T09:12:44.321900Z","latestVersion":"0.2.0"}
2 mo ago · declared · homepageUrl → "https://github.com/beepboop2025/noisefloor"
2 mo ago · declared · license → "MIT"
2 mo ago · declared · summary → "Is this number real, or is it noise? Peek-safe A/B tests, change detection, honest forecasts."

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Evidence

What the publisher says

Capabilities

Where this record came from