cloud-monitoring-metric-selection

name	cloud-monitoring-metric-selection
description	Retrieve, query, and identify relevant Google Cloud Monitoring metric descriptors for a GCP 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.
allowed-tools	["list_metric_descriptors","search_web"]

Metric Selection (Service Query & Local Keyword Filtering)

Use this skill to identify the most relevant Google 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.

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.

Workflow

Step 1: Verify & Auto-Configure MCP

1. Check if any tool matching list_metric_descriptors (e.g. 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 Google 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. Identify the target GCP service prefix (e.g. compute, spanner, bigquery, storage) and the project ID from the resource URI.
2. Extract target metric concepts from the user's prompt (e.g., "CPU", "memory", "bytes scanned", "latency", "connections").
3. Map these concepts to standard Google Cloud Monitoring metric substrings (e.g., cpu, mem, scanned_bytes, latenc, connections).

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 Google 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/") (e.g., 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 (e.g. "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 (e.g., checking for a database label if targeting a database resource). Do not attempt to dynamically match resource type strings directly, as Google 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 (e.g., 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.
  3. Notify the user of the failure and ask for clarification.

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 (i.e., one table per service prefix). The table MUST include the following columns: "Metric Type", "Display Name", "Description", "Metric Kind", "Value Type", and "Monitored Resource Types". Map the fields from the Google Cloud Monitoring list_metric_descriptors tool call response objects directly to the table columns:

• Metric Type: Map to the type field (e.g., 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 (e.g., GAUGE, DELTA, CUMULATIVE).
• Value Type: Map to the valueType field (e.g., INT64, DOUBLE, DISTRIBUTION, BOOL).
• Monitored Resource Types: Map to the monitoredResourceTypes list field (e.g., ["spanner_instance"]).

Example Output Table:

Metric Type	Display Name	Description	Metric Kind	Value Type	Monitored Resource Types
spanner.googleapis.com/instance/cpu/utilization	Instance CPU Utilization	Fraction of allocated CPU currently in use.	GAUGE	DOUBLE	["spanner_instance"]

Reference Documentation & Links

• Google Cloud Monitoring Metric List: GCP Metrics Documentation
• MetricDescriptor MCP Tool Reference: MCP Tools Reference: monitoring.googleapis.com
• Monitoring Filter Syntax Guide: Monitoring Filters