waveguard scan timeseries

Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send

liveverifiedunknown~5s typical

mcpprobedwaveguard

$0.0020

per call

plus $0.001 platform fee per run

Open MCP server; Cracked bills routing only

Provider Upstream docs

$Set up https://cracked-ai-pearl.vercel.app/SKILL.md, then use Cracked to run mcp-com-emergentphysicslab-waveguard /waveguard_scan_timeseries for me.

Input

Field	Type	Description
data*	array	Numeric time-series array, ordered chronologically. Should have at least 3x window_size da
window_size	integer	Number of data points per window (default: 10). Smaller windows detect finer-grained anoma default 10
test_windows	integer	Number of most recent windows to test (default: half of total windows). The rest are used
sensitivity	number	Anomaly sensitivity (default: 1.0). Higher = more sensitive.

Call it

curl

curl https://cracked-ai-pearl.vercel.app/v1/run \
  -H "Authorization: Bearer ck_live_..." -H "content-type: application/json" \
  -d '{"provider":"mcp-com-emergentphysicslab-waveguard","endpoint":"/waveguard_scan_timeseries","input":{"data":[],"window_size":10}}'

cli

npx cracked-ai run -p mcp-com-emergentphysicslab-waveguard -e /waveguard_scan_timeseries -i '{"data":[],"window_size":10}'

mcp

run_tool({ provider: "mcp-com-emergentphysicslab-waveguard", endpoint: "/waveguard_scan_timeseries", input: {"data":[],"window_size":10} })

Try it

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