Cloud Monitoring List Time Series Request
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill assists in constructing valid ListTimeSeries REST API requests for Google Cloud Monitoring. It guides the user through identifying metric descriptors, defining filter expressions, and selecting appropriate aggregation parameters like aligners and reducers. It ensures requests are formatted correctly according to the metric's kind and value type, and includes a validation step using the list_timeseries MCP tool to verify the generated query structure.
What it does
- Generates valid ListTimeSeries REST API request payloads.
- Constructs filter expressions based on metric types and resource labels.
- Determines appropriate perSeriesAligner and crossSeriesReducer settings.
- Calculates alignment periods based on requested time intervals.
- Validates generated request structures against live telemetry.
- Ensures proper attribution for API and CLI requests.
When to use it
- When you need to build a JSON payload for a Cloud Monitoring ListTimeSeries API call.
- When you need to determine the correct aggregation settings for a specific metric.
- When you need to construct a filter string for querying time series data.
- When you need to verify if a constructed monitoring query is syntactically valid.
Advantages
- Ensures adherence to Cloud Monitoring API syntax requirements.
- Automates the selection of aligners and reducers based on metric metadata.
- Provides a structured workflow for resolving metric descriptors.
- Includes built-in validation to confirm request validity before execution.
Usage examples
- “Generate a ListTimeSeries request for compute.googleapis.com/instance/cpu/utilization for project my-gcp-project over the last hour.”
- Returns a JSON object containing the name, filter, interval, and aggregation parameters configured for CPU utilization metrics.
- “What aggregation settings should I use for a GAUGE metric with INT64 value type if I want the mean?”
- Provides the specific perSeriesAligner (ALIGN_MEAN) and crossSeriesReducer (REDUCE_MEAN) to use in the aggregation block.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google/cloud-monitoring-list-time-series-request/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip cloud-monitoring-list-time-series-request.zip -d .agents/skills
Documentation by Google
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).
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:cloud-monitoring-list-time-series-request)"
and append agent-skills/1.0 (skill:cloud-monitoring-list-time-series-request) to User-Agent headers.
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 executingListTimeSeriesrequests. Do NOT use placeholders for project names.
Workflow
Inspect Metric Metadata
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.Discover Missing Metadata: If exact metric descriptors including
metric.type,metricKind, andvalueTypeare 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-selectionskill 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 thelist_metric_descriptorsMCP tool. If the tool is missing, refer to thecloud-monitoring-metric-selectionskill 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.
Identify Key Fields: From the retrieved descriptor, identify key schema attributes:
type: The Cloud Monitoring metric type string.metricKind:GAUGE,DELTA, orCUMULATIVE.valueType:INT64,DOUBLE,DISTRIBUTION, orBOOL.monitoredResourceTypes: Compatibleresource.typestrings, for example["cloudsql_database", "cloudsql_instance"]. If multiple resource types are listed, select the specificresource.typethat matches the target granularity of the user's request.
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:
Single Metric Type Restriction: Every
filterMUST specify exactly onemetric.typeclause using an equality operator. For example:metric.type = "compute.googleapis.com/instance/cpu/utilization"
Monitored Resource Type Filter: MUST include the
resource.typefilter 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"
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.
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"
Resource Name versus ID Resolution:
If the user specifies a human-readable GCE VM instance name such as
"instance-1", butresource.labels.instance_idexpects a numeric ID, you MUST filter using eithermetric.labels.instance_name = "instance-1"ormetadata.system_labels.name = "instance-1".Do NOT use
resource.metadata.nameorresource.metadata.*. This prefix is invalid in Cloud Monitoring filter syntax.Do NOT assign a string instance name directly to
resource.labels.instance_idunless the resource type explicitly uses string IDs.
Database Identifier Labels: Database labels such as
database_idfor Cloud SQL and Spanner, ordataset_idfor BigQuery, use composite keys formatted as<project_id>:<instance_name>. For example:resource.labels.database_id = "my-project:foo".Ops Agent Metrics State Label Filtering: For
agent.googleapis.com/memory/percent_usedandagent.googleapis.com/disk/percent_usedmetrics, you MUST usemetric.labels.state != "free". Do NOT filter bymetric.labels.state = "used".
Choose Aggregation Structure
Select the perSeriesAligner, crossSeriesReducer, groupByFields, and
alignmentPeriod according to the metric properties and visualization goal:
Consult the Aggregations Reference: You MUST include both
perSeriesAlignerandcrossSeriesReducerin theaggregationquery parameters of every request. Read and follow the Cloud Monitoring ListTimeSeries Basic Aggregations Reference to select the exactperSeriesAlignerandcrossSeriesReducercombinations 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 bystate != "free".Grouping Fields and Resource Granularity: When
crossSeriesReduceris specified as anything other thanREDUCE_NONE, list the exact labels to preserve. When querying multi-instance resources like VMs, databases, or subscriptions, include the primary resource identifier ingroupByFields. For example, useresource.labels.instance_idfor VMs orresource.labels.database_idfor databases. This prevents collapsing separate resource streams into a single global aggregate.Alignment Period Determination: Calculate the query lookback duration from
endTimeminusstartTime, ensuringstartTimeprecedesendTime. IfendTime <= startTime, flag an error before computing duration. SetalignmentPeriodaccording 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
alignmentPeriod = "172800s".Omission Rule:
alignmentPeriodis omitted only whenperSeriesAligneris set toALIGN_NONE.
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
aggregationparameters with theperSeriesAligner,crossSeriesReducer,alignmentPeriod, and optionalgroupByFieldsvalues determined during aggregation selection.Interval Requirements:
startTimeandendTimeMUST 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, whereendTimeis the present moment andstartTimeis one hour prior. Do NOT hardcode static dates from examples.Alignment Period Requirement: Determine
alignmentPeriodfrom the lookback duration ofendTimeminusstartTimeusing the mapping above. For the default one-hour lookback interval,alignmentPeriodis"60s".View Requirement: MUST default to
"FULL"when time series data points are needed, or"HEADERS"when inspecting metadata and series identities only.
Validate Request via list_timeseries MCP Tool
You MUST validate the generated request parameters against live Cloud Monitoring
telemetry before returning the final output. Call the list_timeseries MCP tool
passing all generated query parameters (name, filter, interval,
aggregation). When validating you MUST set view="HEADERS" to minimize
latency and payload size while verifying request structure. A response without
API errors confirms that your filter and aggregation settings are valid.
If the list_timeseries tool is unavailable, fall back to a direct API call.
References
Files
- ATTRIBUTION.mdAdded by AutoGineer900 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md11.4 KiB
- references/attribution.md1.9 KiB
- references/basic_aggregations.md10.4 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs an MCP server
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Attribution and licence
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- google/skills · skills/cloud/cloud-monitoring-list-time-series-request · commit 2194d3b
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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