cloud-monitoring-metric-selection

SKILL.md

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.

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

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.

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.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

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-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:

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

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, and
description fields of the descriptors.

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:

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 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 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