Datadog
detect_anomalies
Scan a table for unusual patterns: volume drops/spikes, data gaps, value concentration, high null rates, stale data. Severity-ranked alerts. Tables > 100k rows use a sampled path (~5%) — when a finding has `sampled:true`, surface it to the user with a hedge like 'based on a ~5% sample' rather than presenting the number as exact. Dialect-aware: TABLESAMPLE SYSTEM on postgres, TABLESAMPLE PERCENT on mssql, WHERE RAND() on mysql.
Remote thinair/data
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Input Schema
{
"type": "object",
"properties": {
"table": {
"type": "string",
"description": "Table to scan for anomalies"
},
"connection": {
"type": "string",
"description": "Target connection name from this tenant's inventory. Call `list_connections` to see every name + dialect, then match semantically to the user's intent (e.g. 'analytics' → a connection named `*-analytics-*`; 'prod' → a connection with `prod-` prefix). If the user didn't specify, use the tenant's default (first added). Do not invent names — resolve from `list_connections` output."
},
"date_column": {
"type": "string",
"description": "Date column for trend analysis (auto-detected if omitted)"
}
}
}