Cloud Monitoring Metric Selection
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured process for identifying relevant Google Cloud Monitoring metrics. It guides the agent to verify MCP tool availability, extract service-specific keywords from user requests, and perform targeted API queries to retrieve metric descriptors. The skill ensures accurate data retrieval by enforcing project ID validation and providing a clear, standardized output format for the discovered metrics.
What it does
- Verifies and configures the required Google Cloud Monitoring MCP tool.
- Extracts service prefixes and metric concepts from natural language prompts.
- Executes dynamic API queries to fetch up-to-date metric descriptors.
- Filters retrieved metric data locally based on user-provided keywords.
- Formats discovered metrics into clean, readable Markdown tables.
When to use it
- When you need to find specific metric types for a GCP service like Compute Engine or BigQuery.
- When you need to understand the schema, kind, or unit of a particular cloud metric.
- When you are unsure which metrics are available for monitoring a specific resource.
- When you need to generate a list of relevant metrics for a monitoring dashboard or alert.
Advantages
- Ensures data accuracy by querying live API descriptors rather than relying on static documentation.
- Reduces cognitive load by filtering large metric lists into relevant subsets.
- Provides consistent, structured output for easy integration into reports or documentation.
- Includes built-in safeguards for project ID verification and API error handling.
Usage examples
- “Find all metrics related to CPU and memory for my Compute Engine instances in project my-gcp-project.”
- The agent will query the Cloud Monitoring API for compute.googleapis.com metrics, filter them for CPU and memory keywords, and present a table with the relevant metric types, descriptions, and units.
- “What metrics are available to track write throughput in Cloud Spanner?”
- The agent will identify the Spanner service prefix, query the API for Spanner-specific metrics, and return a table highlighting metrics related to write operations.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/cloud-monitoring-metric-selection/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip cloud-monitoring-metric-selection.zip -d .agents/skills
Documentation by Google
Metric Selection (Service Query & Local Keyword Filtering)
Use this skill to identify the most relevant Cloud Monitoring metric descriptors. It queries all metric descriptors for a target service from the API and filters them locally inside the agent's context using keyword matching.
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-metric-selection)"
and append agent-skills/1.0 (skill:cloud-monitoring-metric-selection) to User-Agent headers.
CRITICAL RULES
Always Query Live APIs: You MUST always retrieve the most up-to-date metric descriptors dynamically by calling the
list_metric_descriptorsMCP tool.Mandatory Project ID and Resource Parameter Clarification: BEFORE calling any API tools (such as
list_metric_descriptors), you MUST ensure the GCP Project ID is provided in the prompt, URI, or environment context. If the Project ID cannot be resolved, you MUST ask the user to clarify or provide it BEFORE executing API queries. Do NOT run API queries against unconfirmed default or placeholder project names (such asmock-project,my-project-id,unused, orYOUR_PROJECT_ID).Fallback Reporting: If API calls fail and fallback sources (such as public docs) are used, you MUST state the error, the fallback source, and the risks of non-live data (such as potential staleness, missing custom metrics, or schema mismatches).
Workflow
Step 1: Verify & Auto-Configure MCP
Check if any tool matching
list_metric_descriptors(such asgoogle-cloud-monitoring:list_metric_descriptors,mcp_google-cloud-monitoring_list_metric_descriptors, or a similar pattern) is available in your active toolset.Verify via Unique URL: To ensure you are calling the correct Cloud Monitoring tool, confirm that the underlying MCP server configuration points to:
https://monitoring.googleapis.com/mcp.If the tool is missing:
Locate the MCP configuration file for the user's environment. Check common paths:
~/.gemini/config/mcp_config.json~/.codeium/windsurf/mcp_config.jsoncline_mcp_settings.jsonclaude_desktop_config.json
Directly update/merge the configuration file with the following server configuration. CRITICAL: Merge the JSON object to preserve any existing MCP servers in
mcpServers. Do not overwrite the file."google-cloud-monitoring": { "url": "https://monitoring.googleapis.com/mcp", "authProviderType": "google_credentials", "enabledTools": [ "list_metric_descriptors" ] }Print a clear message notifying the user that the
google-cloud-monitoringMCP server has been configured, and request them to restart or start a new chat session to refresh tools. Stop calling further tools and end the turn.
Step 2: Analyze Request & Extract Keywords
Resolve Project ID and Identifiers: Check for the GCP Project ID and resource identifiers in the prompt, resource URIs, or environment context. According to the CRITICAL RULES above, do NOT use placeholder project names.
Identify Service Prefix: Map target GCP services to their standard prefix (such as
compute,spanner,bigquery,storage).Extract Metric Concepts: Extract metric keywords from user prompt (such as "CPU", "memory", "bytes scanned", "latency", "connections") and map to search substrings.
Example Query Analysis:
User Prompt: "Check Cloud Storage bucket write throughput and request count"
Resource URI:
//storage.googleapis.com/projects/my-project/buckets/my-bucketService Prefix:
storage(mapped tostorage.googleapis.com)Metric Keywords:
write,throughput,request,countMapped Substrings:
write,throughput,request_count,count
Step 3: Query Metric Descriptors via list_metric_descriptors Tool
Query all metric descriptors for each identified service prefix using the
list_metric_descriptors MCP tool (using pageSize: 200). Because Cloud
Monitoring filters do not allow combining multiple metric.type restrictions
with OR, you must initiate a separate query for each identified service
prefix (either sequentially or in parallel).
If any response includes a nextPageToken, you MUST make consecutive follow-up
calls passing pageToken until all remaining descriptors for that prefix are
retrieved before filtering.
Filter Pattern Construction: Map the target service domain to its appropriate prefix style:
Standard Google Cloud Services:
starts_with("<service_prefix>.googleapis.com/")(such asbigquery.googleapis.com/,redis.googleapis.com/).Ops Agent (Guest OS):
starts_with("agent.googleapis.com/")(for guest OS memory/disk metrics).Kubernetes / GKE Native:
starts_with("kubernetes.io/")Istio Service Mesh:
starts_with("istio.io/")Knative Serving / Autoscaler:
starts_with("knative.dev/")Custom / External Metrics: Use
starts_with("custom.googleapis.com/")orstarts_with("external.googleapis.com/").
Example Tool Call Payload: If both Spanner and Compute Engine are targeted in the request, execute these two tool calls:
Spanner query:
{
"name": "projects/my-project-id",
"filter": "metric.type = starts_with(\"spanner.googleapis.com/\")",
"pageSize": 200
}Compute Engine query:
{
"name": "projects/my-project-id",
"filter": "metric.type = starts_with(\"compute.googleapis.com/\")",
"pageSize": 200
}Call the list_metric_descriptors tool with these payloads.
Step 4: Local Filtering & Fallback Protocol
Aggregate all descriptors returned from Step 3, and filter them locally inside your LLM context:
Keyword Filtering: Filter the list by matching your target metric keywords (such as "cpu", "latency") against the
type,displayName, anddescriptionfields of the descriptors.Resource Alignment: Check if the metric contains labels matching the target resource granularity (such as checking for a
databaselabel if targeting a database resource). Do not attempt to dynamically match resource type strings directly, as Cloud Monitoring resource mappings (like Spanner databases mapping tospanner_instance) can be counter-intuitive.
Troubleshooting & API Fallbacks
If any tool call fails, times out, or returns empty results, use these strategies:
Case A: API Syntax Error: Examine the error message, correct the filter syntax, and retry.
Case B: Timeout / Rate Limits: Retry the call once with a smaller page size (such as
pageSize: 20).Case C: Unrecoverable Failure / Empty List:
Verify if the target service is enabled in the project.
Search Google Cloud public documentation to verify standard metrics for the service.
Step 5: Output Selected Metrics
For each service domain, return only the 5-15 key metrics directly relevant to the user's intent.
You MUST report the selected metrics in clean Markdown tables, grouped by
service (that is, one table per service prefix). The table MUST include the
following columns: "Metric Type", "Display Name", "Description", "Metric Kind",
"Value Type", "Unit", and "Monitored Resource Types". Map the fields from the
Cloud Monitoring list_metric_descriptors tool call response objects
directly to the table columns:
Metric Type: Map to the
typefield (for example,spanner.googleapis.com/instance/cpu/utilization).Display Name: Map to the
displayNamefield.Description: Map to the
descriptionfield.Metric Kind: Map to the
metricKindfield (for example,GAUGE,DELTA,CUMULATIVE).Value Type: Map to the
valueTypefield (for example,INT64,DOUBLE,DISTRIBUTION,BOOL).Unit: Map to the
unitfield (for example,1,By,s,ms).Monitored Resource Types: Map to the
monitoredResourceTypeslist field (for example,["spanner_instance"]).
Example Output Table:
| Metric Type | Display Name | Description | Metric Kind | Value Type | Unit | Monitored Resource Types |
|---|---|---|---|---|---|---|
spanner.googleapis.com/instance/cpu/utilization | Instance CPU Utilization | Fraction of allocated CPU currently in use. | GAUGE | DOUBLE | 1 | ["spanner_instance"] |
Reference Documentation & Links
Cloud Monitoring Metric List: GCP Metrics Documentation
MetricDescriptor MCP Tool Reference: MCP Tools Reference: monitoring.googleapis.com
Monitoring Filter Syntax Guide: Monitoring Filters
Files
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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
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server
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- google/skills · skills/cloud/cloud-monitoring-metric-selection · commit 2194d3b
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