cloud-monitoring-list-time-series-request

name	cloud-monitoring-list-time-series-request
metadata	{"category":"CloudObservabilityAndMonitoring"}
description	Generate valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.

Cloud Monitoring ListTimeSeries Request Generator

Use this skill to translate any Cloud Monitoring metric descriptor into valid, production-ready ListTimeSeries REST API query parameters (name, filter, interval.startTime, interval.endTime, aggregation.*, view).

CRITICAL RULES

• Mandatory Project ID Clarification: You MUST ensure the GCP Project ID is present in the user prompt, input payload, or environment context (such as via gcloud config get-value project). If the Project ID is missing and cannot be resolved, you MUST ask the user to clarify it before generating or executing ListTimeSeries requests. Do NOT use placeholders for project names.

Workflow

Inspect Metric Metadata

1. Use Provided Metric Metadata First: If the user's prompt already includes metric metadata such as metric.type, metricKind, valueType, resource types, or label keys, use those values directly instead of calling API tools.
2. Discover Missing Metadata: If exact metric descriptors including metric.type, metricKind, and valueType are missing or underspecified, resolve the target metric's descriptor using one of these paths:
   • Vague Query: If the prompt is vague, such as asking for VM CPU usage, use the cloud-monitoring-metric-selection skill first to identify the specific metric type.
   • Known Metric Type: If you already have the specific metric type name such as compute.googleapis.com/instance/cpu/utilization, but need its descriptor, call the list_metric_descriptors MCP tool. If the tool is missing, refer to the cloud-monitoring-metric-selection skill to configure the Cloud Monitoring MCP server.
   • Fallback: If the MCP tool cannot be configured, fall back to making a direct Cloud Monitoring API call.
3. Identify Key Fields: From the retrieved descriptor, identify key schema attributes:
   • type: The Cloud Monitoring metric type string.
   • metricKind: GAUGE, DELTA, or CUMULATIVE.
   • valueType: INT64, DOUBLE, DISTRIBUTION, or BOOL.
   • monitoredResourceTypes: Compatible resource.type strings, for example ["cloudsql_database", "cloudsql_instance"]. If multiple resource types are listed, select the specific resource.type that matches the target granularity of the user's request.

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Construct Monitoring Filter

The filter parameter is a mandatory string in Cloud Monitoring syntax that restricts the query to a single metric.type and optional resource and metric labels:

1. Single Metric Type Restriction: Every filter MUST specify exactly one metric.type clause using an equality operator. For example:

   • metric.type = "compute.googleapis.com/instance/cpu/utilization"

2. Monitored Resource Type Filter: MUST include the resource.type filter when the target resource granularity is known, preventing collisions across services that share metric types or sub-resources. For example:

   • metric.type = "cloudsql.googleapis.com/database/cpu/utilization" AND resource.type = "cloudsql_database"

3. Preserve User Literals and IDs: You MUST use literal resource names, IDs, zones, and project parameters provided by the user without alteration. Do NOT override or replace user-specified identifiers with active resources found during metric metadata discovery unless explicitly requested.

4. Label Type Prefixing:

   • Prefix resource-level dimensions, such as instance ID, zone, project, database ID, or subscription ID, with the resource.labels. prefix. For example:
     • resource.labels.instance_id = "123456789"
     • resource.labels.database_id = "my-project:my-instance"
   • Prefix metric-level dimensions, such as state, command, response code, or instance name metadata when stored on the metric, with the metric.labels. prefix. For example:
     • metric.labels.state != "free"
     • metric.labels.instance_name = "instance-1"

5. Resource Name versus ID Resolution:

   • If the user specifies a human-readable GCE VM instance name such as "instance-1", but resource.labels.instance_id expects a numeric ID, you MUST filter using either metric.labels.instance_name = "instance-1" or metadata.system_labels.name = "instance-1".
   • Do NOT use resource.metadata.name or resource.metadata.*. This prefix is invalid in Cloud Monitoring filter syntax.
   • Do NOT assign a string instance name directly to resource.labels.instance_id unless the resource type explicitly uses string IDs.

6. Database Identifier Labels: Database labels such as for Cloud SQL and Spanner, or for BigQuery, use composite keys formatted as . For example: .

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Choose Aggregation Structure

Select the perSeriesAligner, crossSeriesReducer, groupByFields, and alignmentPeriod according to the metric properties and visualization goal:

1. Consult the Aggregations Reference: You MUST include both perSeriesAligner and crossSeriesReducer in the aggregation query parameters of every request. Read and follow the Cloud Monitoring ListTimeSeries Basic Aggregations Reference to select the exact perSeriesAligner and crossSeriesReducer combinations for your metric's Metric Kind and Value Type pairing, and to apply mandatory SRE rules for utilization metrics, counters, distributions, and state-based gauges such as memory filtered by state != "free".
2. Grouping Fields and Resource Granularity: When crossSeriesReducer is specified as anything other than REDUCE_NONE, list the exact labels to preserve. When querying multi-instance resources like VMs, databases, or subscriptions, include the primary resource identifier in groupByFields. For example, use resource.labels.instance_id for VMs or resource.labels.database_id for databases. This prevents collapsing separate resource streams into a single global aggregate.
3. Alignment Period Determination: Calculate the query lookback duration from endTime minus startTime, ensuring startTime precedes endTime. If endTime <= startTime, flag an error before computing duration. Set alignmentPeriod according to Cloud Console default fine granularity standards:
   • Duration <= 110 minutes: Set alignmentPeriod = "60s".
   • Duration <= 23 hours: Set alignmentPeriod = "300s".
   • Duration <= 6 days: Set alignmentPeriod = "3600s".
   • Duration <= 23 days: Set alignmentPeriod = "10800s".
   • Duration <= 80 days: Set alignmentPeriod = "21600s".
   • Duration <= 180 days: Set alignmentPeriod = "43200s".
   • Duration <= 350 days: Set alignmentPeriod = "86400s".
   • Duration <= 500 days: Set .

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Format Valid Request

Present the generated ListTimeSeries REST query parameters. For example:

{
  "name": "projects/<project_id>",
  "filter": "metric.type = \"<metric_type>\" AND resource.type = \"<resource_type>\"",
  "interval": {
    "startTime": "<iso_8601_start>",
    "endTime": "<iso_8601_end>"
  },
  "aggregation": {
    "alignmentPeriod": "60s",
    "perSeriesAligner": "ALIGN_RATE",
    "crossSeriesReducer": "REDUCE_SUM",
    "groupByFields": [
      "resource.labels.zone"
    ]
  },
  "view": "FULL"
}

• Aggregation Requirements: Populate the aggregation parameters with the perSeriesAligner, crossSeriesReducer, alignmentPeriod, and optional groupByFields values determined during aggregation selection.
• Interval Requirements: startTime and endTime MUST be valid RFC 3339 and ISO 8601 timestamps such as "YYYY-MM-DDTHH:MM:SSZ". If not explicitly provided by the user, dynamically compute a one-hour lookback interval ending at the current time, where endTime is the present moment and startTime is one hour prior. Do NOT hardcode static dates from examples.
• Alignment Period Requirement: Determine alignmentPeriod from the lookback duration of endTime minus startTime using the mapping above. For the default one-hour lookback interval, alignmentPeriod is "60s".
• View Requirement: MUST default to "FULL" when time series data points are needed, or "HEADERS" when inspecting metadata and series identities only.

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Validate Request via REST API

Always validate the generated request parameters against live Cloud Monitoring telemetry before returning the final output. DO NOT call the list_timeseries MCP tool. Perform an HTTP GET request directly to the Cloud Monitoring v3 REST API using curl -s -H "Authorization: Bearer \$(gcloud auth print-access-token)" -G with --data-urlencode for all query fields (name, filter, interval.startTime, interval.endTime, aggregation.alignmentPeriod, aggregation.perSeriesAligner, aggregation.crossSeriesReducer, and view=HEADERS). An HTTP 200 OK response confirms that your filter and aggregation settings are valid.

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References

• Cloud Monitoring ListTimeSeries Basic Aggregations Reference
• Cloud Monitoring Monitored Resource Types Reference
• Cloud Monitoring Filter Syntax
• Cloud Monitoring REST API Reference: projects.timeSeries.list