cloud-monitoring-metric-selection

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---
name: cloud-monitoring-metric-selection
metadata:
  category: CloudObservabilityAndMonitoring
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.
---

# 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.
*   **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 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.

        ```json
        "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 reque