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Cloud Monitoring Metric Selection

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  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
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From the author

Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.
— the description by Google

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/cloud-monitoring-metric-selection/download
  2. Unzip 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_descriptors MCP 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 as mock-project, my-project-id, unused, or YOUR_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
  1. Check if any tool matching list_metric_descriptors (such as google-cloud-monitoring:list_metric_descriptors, mcp_google-cloud-monitoring_list_metric_descriptors, or a similar pattern) is available in your active toolset.

  2. 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.

  3. 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.json

      • cline_mcp_settings.json

      • claude_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-monitoring MCP 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
  1. 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.

  2. Identify Service Prefix: Map target GCP services to their standard prefix (such as compute, spanner, bigquery, storage).

  3. 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-bucket

  • Service Prefix: storage (mapped to storage.googleapis.com)

  • Metric Keywords: write, throughput, request, count

  • Mapped 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:

  1. Standard Google Cloud Services: starts_with("<service_prefix>.googleapis.com/") (such as bigquery.googleapis.com/, redis.googleapis.com/).

  2. Ops Agent (Guest OS): starts_with("agent.googleapis.com/") (for guest OS memory/disk metrics).

  3. Kubernetes / GKE Native: starts_with("kubernetes.io/")

  4. Istio Service Mesh: starts_with("istio.io/")

  5. Knative Serving / Autoscaler: starts_with("knative.dev/")

  6. Custom / External Metrics: Use starts_with("custom.googleapis.com/") or starts_with("external.googleapis.com/").

Example Tool Call Payload: If both Spanner and Compute Engine are targeted in the request, execute these two tool calls:

  1. Spanner query:

{
  "name": "projects/my-project-id",
  "filter": "metric.type = starts_with(\"spanner.googleapis.com/\")",
  "pageSize": 200
}
  1. 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:

  1. Keyword Filtering: Filter the list by matching your target metric keywords (such as "cpu", "latency") against the type, displayName, and description fields of the descriptors.

  2. Resource Alignment: Check if the metric contains labels matching the target resource granularity (such as checking for a database label if targeting a database resource). Do not attempt to dynamically match resource type strings directly, as Cloud Monitoring resource mappings (like Spanner databases mapping to spanner_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:

    1. Verify if the target service is enabled in the project.

    2. 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 type field (for example, spanner.googleapis.com/instance/cpu/utilization).

  • Display Name: Map to the displayName field.

  • Description: Map to the description field.

  • Metric Kind: Map to the metricKind field (for example, GAUGE, DELTA, CUMULATIVE).

  • Value Type: Map to the valueType field (for example, INT64, DOUBLE, DISTRIBUTION, BOOL).

  • Unit: Map to the unit field (for example, 1, By, s, ms).

  • Monitored Resource Types: Map to the monitoredResourceTypes list field (for example, ["spanner_instance"]).

Example Output Table:

Metric TypeDisplay NameDescriptionMetric KindValue TypeUnitMonitored Resource Types
spanner.googleapis.com/instance/cpu/utilizationInstance CPU UtilizationFraction of allocated CPU currently in use.GAUGEDOUBLE1["spanner_instance"]

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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Attribution and licence

Published by
Google
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Google (Official)
Source
google/skills · skills/cloud/cloud-monitoring-metric-selection · 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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