NoiseFloor — market attention and evidence checks
mcp remote · streamable-http · v0.4.3 · source: crawl
https://api.seiche.info/noisefloor/mcp
https://github.com/beepboop2025/noisefloor
Offline market diagnostics, news repetition, data coverage and statistical checks for AI agents.
Works
100
Verified Oct 31, 2026 · p50 1196 ms · p95 1329 ms
Worth
no data
No outcomes reported yet. Agents report through report_outcome after a find_agents call.
Checks
| Check | Weight | Result | Evidence |
|---|---|---|---|
| reachable | 20 | pass | 3 of 3 handshakes succeeded; server 'noisefloor' |
| tools_listed | 15 | pass | 10 tools |
| schema_complete | 15 | pass | 10 of 10 tools have a description and a typed schema |
| latency_p50 | 10 | pass | p50 1196 ms |
| latency_p95 | 5 | pass | p95 1329 ms |
| error_free | 10 | pass | 0 of 3 attempts failed |
| auth_posture | 10 | pass | https, open |
| injection_scan | 10 | pass | no patterns found |
| scope | 5 | pass | no execution-style tools |
Unknown counts as fail; n/a is left out of the denominator. How the weights work.
Tools (10)
| Name | Description | Schema |
|---|---|---|
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 constant conditional success probabilities; | typed |
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 a change-review queue; choose real_or_sa | typed |
dyson_reference | Generate a seeded synthetic real-symmetric Brownian-matrix path to illustrate eigenvalue repulsion (beta=1). Use for reproducible numerical education or research references; choose spectral_assessment | typed |
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 interval. Use for a one-step baseline or | typed |
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 coverage. Use when provenance and freshnes | typed |
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 possible corrections; choose market_asses | typed |
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 dashboard metric and its coverage denom | typed |
score_forecasts | Backtest NoiseFloor's own one-step forecast on chronological historical prefixes, issuing before observing and then updating without hindsight. Use to evaluate forecast_next's coverage and misses; thi | typed |
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 series; choose market_assessment for indiv | typed |
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 stays empty. Set valid_evalues=true onl | typed |
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