playbook/antigravity-awesome-skills/skills/monte-carlo-monitor-creation/references/metric-monitor.md

Metric Monitor Reference

Detailed reference for building createMetricMonitorMac tool calls.

When to Use

Use a metric monitor when the user wants to:

• Track row count changes over time
• Monitor null rates, unique counts, or other statistical metrics on specific fields
• Detect anomalies in numeric distributions (mean, max, min, percentiles)
• Monitor data freshness (time since last row count change)
• Segment metrics by dimensions (e.g., by country, status)

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Required Parameters

Parameter	Type	Description
name	string	Unique identifier for the monitor. Use a descriptive slug (e.g., orders_null_check).
description	string	Human-readable description of what the monitor checks.
table	string	Table MCON (preferred) or database:schema.table format. If not MCON, also pass warehouse.
aggregate_time_field	string	MUST be a real timestamp/datetime column from the table. NEVER guess this value.
alert_conditions	array	List of alert condition objects (see Alert Conditions below).

Optional Parameters

Parameter	Type	Default	Description
warehouse	string	auto-resolved	Warehouse name or UUID. Required if table is not an MCON.
segment_fields	array of string	none	Fields to group/segment metrics by (e.g., ["country", "status"]).
aggregate_by	string	"day"	Time interval: "hour", "day", "week", "month".
where_condition	string	none	SQL WHERE clause (without WHERE keyword) to filter rows before computing metrics.
interval_minutes	int	auto	Schedule interval in minutes. Must be compatible with aggregate_by (see note below). If not specified, the tool defaults to the minimum valid interval for the chosen aggregate_by.
domain_id	string (uuid)	none	Domain UUID (use getDomains to list).

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Schedule and Aggregation Compatibility

The schedule interval must be compatible with aggregate_by. Daily aggregation requires an interval that is a multiple of 1440 minutes (24 hours), weekly requires a multiple of 10080, etc. If you pass interval_minutes, make sure it satisfies this constraint. If you omit it, the tool picks a sensible default.

aggregate_by	Minimum interval_minutes	Default if omitted
hour	60	60
day	1440	1440
week	10080	10080
month	43200	43200

For example, to run a daily-aggregated monitor every other day, pass aggregate_by: "day" and interval_minutes: 2880.

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Choosing the Timestamp Field

The aggregate_time_field is the most critical parameter. It MUST be a real column from the table that contains timestamp or datetime values. This is the number one source of monitor creation failures.

How to pick it

1. You should already have the column names from getTable with include_fields: true (done in Step 2 of the main skill).
2. Look for columns whose names suggest a timestamp: created_at, updated_at, modified_at, timestamp, event_timestamp, or columns with _ts, _dt, _time suffixes, or date, datetime.
3. If the user specified one, verify it exists in the column list.
4. If exactly one obvious candidate exists, suggest it.
5. If multiple candidates exist, present them and ask the user.
6. If NO obvious timestamp columns exist, suggest a custom SQL monitor instead (which does not need a timestamp field).

NEVER proceed without confirming the timestamp field exists in the table schema.

Common timestamp field mistakes

• Using a DATE column (not TIMESTAMP): This may work, but aggregation granularity is limited. For example, aggregate_by: "hour" is meaningless on a DATE column because the time component is always midnight. Warn the user and default to aggregate_by: "day" or coarser.
• Using a field that contains many nulls: If the timestamp column has significant null values, rows with null timestamps are excluded from aggregation windows, producing unreliable or misleading results. Check the column's null rate from getTable field stats if available, and warn the user if it is high.
• Guessing a field name that does not exist: Always verify the column name against the getTable output. A typo or assumed name (e.g., created_date when the actual column is created_at) causes the monitor creation to fail silently or error.

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Field-Type-to-Metric Compatibility Matrix

Before selecting a metric, check the column's data type from getTable results. Passing a metric incompatible with the column type is the most common source of creation failures after timestamp issues.

Column Type	Compatible Metrics
Numeric (int, float, decimal, bigint)	NUMERIC_MEAN, NUMERIC_MEDIAN, NUMERIC_MIN, NUMERIC_MAX, NUMERIC_STDDEV, SUM, ZERO_COUNT, ZERO_RATE, NEGATIVE_COUNT, NEGATIVE_RATE, NULL_COUNT, NULL_RATE, UNIQUE_COUNT, UNIQUE_RATE, DUPLICATE_COUNT
String / Text (varchar, char, text)	TEXT_MAX_LENGTH, TEXT_MIN_LENGTH, TEXT_MEAN_LENGTH, TEXT_INT_RATE, TEXT_NUMBER_RATE, TEXT_UUID_RATE, TEXT_EMAIL_ADDRESS_RATE, EMPTY_STRING_COUNT, EMPTY_STRING_RATE, NULL_COUNT, NULL_RATE, UNIQUE_COUNT, UNIQUE_RATE, DUPLICATE_COUNT
Boolean	TRUE_COUNT, FALSE_COUNT, NULL_COUNT, NULL_RATE
Timestamp / Date	FUTURE_TIMESTAMP_COUNT, PAST_TIMESTAMP_COUNT, UNIX_ZERO_TIMESTAMP_COUNT, NULL_COUNT, NULL_RATE, UNIQUE_COUNT, UNIQUE_RATE
Any type	NULL_COUNT, NULL_RATE, UNIQUE_COUNT, UNIQUE_RATE, DUPLICATE_COUNT

Rules

• NEVER apply NUMERIC_*, SUM, ZERO_*, or NEGATIVE_* metrics to string, boolean, or timestamp columns.
• NEVER apply TEXT_* or EMPTY_STRING_* metrics to numeric, boolean, or timestamp columns.
• NEVER apply TRUE_COUNT or FALSE_COUNT to non-boolean columns.
• NEVER apply FUTURE_TIMESTAMP_COUNT, PAST_TIMESTAMP_COUNT, or UNIX_ZERO_TIMESTAMP_COUNT to non-timestamp columns.
• When in doubt, NULL_COUNT, NULL_RATE, UNIQUE_COUNT, and UNIQUE_RATE are safe for any column type.

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Alert Conditions

Each alert condition has:

Field	Type	Required	Description
metric	string	Yes	The metric to monitor (see Metrics Reference below).
operator	string	Yes	"AUTO" (anomaly detection), "GT", "LT", "EQ", "GTE", "LTE", "NE".
threshold	number	For explicit operators	The threshold value. Required when using GT, LT, EQ, GTE, LTE, or NE. Not used with AUTO.
fields	array of string	Depends	Column names to apply the metric to. Required for field-level metrics. Not needed for table-level metrics.

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Operator Guidance

When to use AUTO (anomaly detection)

• Best when you do not know the expected range of values and want Monte Carlo's ML to learn normal patterns and alert on deviations.
• Works well for organic metrics that vary day-to-day (row counts, null rates on evolving data, numeric distributions).
• Some metrics require AUTO -- see the table below.

When to use explicit thresholds (GT, LT, EQ, GTE, LTE, NE)

• Use when there is a known business rule or data contract (e.g., "null rate on email should never exceed 5%", "order amount must always be greater than 0").
• Provides deterministic alerting -- no training period needed, alerts fire immediately when the condition is met.
• Requires a threshold value in the alert condition.

Operator restrictions by metric

Metric	Allowed Operators	Notes
ROW_COUNT_CHANGE	AUTO only	Anomaly detection on row count delta.
TIME_SINCE_LAST_ROW_COUNT_CHANGE	AUTO only	Anomaly detection on staleness duration.
RELATIVE_ROW_COUNT	AUTO only	Anomaly detection on segment distribution. Requires segment_fields.
All other metrics	AUTO, GT, LT, EQ, GTE, LTE, NE	Any operator is valid.

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Metrics Reference

Table-level metrics (no fields needed)

Metric	Operator	Description
ROW_COUNT_CHANGE	Must use AUTO	Alert on anomalous changes in total row count.
TIME_SINCE_LAST_ROW_COUNT_CHANGE	Must use AUTO	Alert when the table has not been updated for an unusual duration.

Field-level metrics (must specify fields)

Metric	Column Types	Description
NULL_COUNT	Any	Count of null values.
NULL_RATE	Any	Rate of null values (0.0 to 1.0).
UNIQUE_COUNT	Any	Count of distinct values.
UNIQUE_RATE	Any	Rate of distinct values (0.0 to 1.0).
DUPLICATE_COUNT	Any	Count of duplicate (non-unique) values.
EMPTY_STRING_COUNT	String/Text	Count of empty string values.
EMPTY_STRING_RATE	String/Text	Rate of empty string values.
NUMERIC_MEAN	Numeric	Mean of numeric field.
NUMERIC_MEDIAN	Numeric	Median of numeric field.
NUMERIC_MIN	Numeric	Minimum value of numeric field.
NUMERIC_MAX	Numeric	Maximum value of numeric field.
NUMERIC_STDDEV	Numeric	Standard deviation of numeric field.
SUM	Numeric	Sum of numeric field.
ZERO_COUNT	Numeric	Count of zero values.
ZERO_RATE	Numeric	Rate of zero values.
NEGATIVE_COUNT	Numeric	Count of negative values.
NEGATIVE_RATE	Numeric	Rate of negative values.
TRUE_COUNT	Boolean	Count of true values.
FALSE_COUNT	Boolean	Count of false values.
TEXT_MAX_LENGTH	String/Text	Maximum string length.
TEXT_MIN_LENGTH	String/Text	Minimum string length.
TEXT_MEAN_LENGTH	String/Text	Mean string length.
TEXT_INT_RATE	String/Text	Rate of values parseable as integers.
TEXT_NUMBER_RATE	String/Text	Rate of values parseable as numbers.
TEXT_UUID_RATE	String/Text	Rate of values matching UUID format.
TEXT_EMAIL_ADDRESS_RATE	String/Text	Rate of values matching email format.
FUTURE_TIMESTAMP_COUNT	Timestamp/Date	Count of timestamps in the future.
PAST_TIMESTAMP_COUNT	Timestamp/Date	Count of timestamps unreasonably far in the past.
UNIX_ZERO_TIMESTAMP_COUNT	Timestamp/Date	Count of timestamps equal to Unix epoch zero (1970-01-01).

Segmentation metric

Metric	Operator	Description
RELATIVE_ROW_COUNT	Must use AUTO	Alert on anomalous changes in distribution across segments. MUST use segment_fields.

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Examples

Row count anomaly detection

{
  "name": "orders_row_count",
  "description": "Detect anomalous changes in daily order volume",
  "table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++analytics:core.orders",
  "aggregate_time_field": "created_at",
  "aggregate_by": "day",
  "alert_conditions": [
    {
      "metric": "ROW_COUNT_CHANGE",
      "operator": "AUTO"
    }
  ]
}

Null monitoring on specific fields

{
  "name": "orders_null_check",
  "description": "Alert when email or user_id nulls exceed 50 per day",
  "table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++analytics:core.orders",
  "aggregate_time_field": "created_at",
  "aggregate_by": "day",
  "alert_conditions": [
    {
      "metric": "NULL_COUNT",
      "operator": "GT",
      "threshold": 50,
      "fields": ["email", "user_id"]
    }
  ]
}

Segmented monitoring

{
  "name": "orders_by_country_distribution",
  "description": "Detect anomalous shifts in order distribution across countries",
  "table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++analytics:core.orders",
  "aggregate_time_field": "created_at",
  "aggregate_by": "day",
  "segment_fields": ["country"],
  "alert_conditions": [
    {
      "metric": "RELATIVE_ROW_COUNT",
      "operator": "AUTO"
    }
  ]
}

Numeric range monitoring with filter

{
  "name": "completed_orders_amount_check",
  "description": "Detect anomalous max order amounts for completed orders",
  "table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++analytics:core.orders",
  "aggregate_time_field": "created_at",
  "aggregate_by": "day",
  "where_condition": "status = 'completed'",
  "alert_conditions": [
    {
      "metric": "NUMERIC_MAX",
      "operator": "AUTO",
      "fields": ["amount"]
    }
  ]
}

Multiple alert conditions in one monitor

{
  "name": "payments_quality_check",
  "description": "Monitor payment amount stats and null rate on transaction_id",
  "table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++warehouse:billing.payments",
  "aggregate_time_field": "processed_at",
  "aggregate_by": "day",
  "domain_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "alert_conditions": [
    {
      "metric": "NUMERIC_MEAN",
      "operator": "AUTO",
      "fields": ["amount"]
    },
    {
      "metric": "NULL_RATE",
      "operator": "GT",
      "threshold": 0.01,
      "fields": ["transaction_id"]
    }
  ]
}