google-ads-api-account-diagnostics

Imported from google/skills (skills/ads/google-ads-api-account-diagnostics/SKILL.md) via skills.sh. Install upstream with npx skills add google/skills --skill google-ads-api-account-diagnostics. Copyright stays with the author.

name: google-ads-api-account-diagnostics description: >- Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate instead). compatibility: Google Ads MCP Server (tools: search, list_accessible_customers, get_resource_metadata); see the google-ads-api-mcp-setup skill. metadata: category: GoogleAds author: google-ads-api-team version: "1.0.1"

Google Ads API Account Performance Diagnostics Skill

This skill provides instructions on how to use the Google Ads MCP server tools to diagnose common account performance issues.

Workflows

Pre-Flight Tool Verification

Before starting any diagnostic task, verify whether the Google Ads MCP Server tools (search, list_accessible_customers, get_resource_metadata — these may be exposed with a server prefix, e.g. customers_list_accessible_customers) are available in the runtime environment:

• If MCP tools are available: Proceed with live diagnostics — first identify active client accounts, then run GAQL via the search tool (arguments customer_id and query).
• If MCP tools are missing: Follow Fallback When MCP Tools Are Unavailable.

Identifying Active Client Accounts

Most diagnostics tasks require sending GAQL queries to a specific customer account. If the customer ID is not explicitly provided by the user, you must first call the list_accessible_customers (or customers_list_accessible_customers) tool to retrieve the customer resource names/IDs you have access to.

Once you have the list of accessible customer IDs, query the customer_client resource using the search tool under those customer accounts to find active client customer accounts. Make sure to select only enabled client accounts and filter out manager accounts:

SELECT
  customer_client.id,
  customer_client.descriptive_name,
  customer_client.status,
  customer_client.manager
FROM customer_client
WHERE customer_client.status = 'ENABLED' AND customer_client.manager = FALSE

Only run subsequent diagnostic queries against the enabled client customer IDs retrieved from this list. Do not query deactivated or manager accounts, as doing so will cause API errors.

Fallback When MCP Tools Are Unavailable

When Google Ads MCP tools (search, list_accessible_customers, get_resource_metadata — these may be exposed with a server prefix, e.g. customers_list_accessible_customers) are not registered in the runtime environment:

1. Inform the User: State that live account performance diagnostics require the Google Ads MCP Server.
2. Reference Setup Guide: Direct the user to the setup instructions in the google-ads-api-mcp-setup skill to install and configure the Google Ads MCP Server for their assistant environment.
3. Provide GAQL Templates, Gotchas, and Remediation Guidance: Formulate and output the complete diagnostic plan for manual execution in the Google Ads Query Builder or via API integration:
   • Provide all multi-step GAQL queries for the workflow, including current-vs-prior date window comparisons (e.g. traffic, impression share, conversion breakdowns, or change events).
   • Explain all metric conversion and constraint gotchas (metrics.cost_micros / 1,000,000 for currency, impression share reported as decimals or "< 0.10", change_event 30-day window and LIMIT 10000 without metrics.*, and successful_event_count / total_event_count plus status for upload health).
   • Outline concrete actionable next steps for each diagnostic branch (e.g., raising campaign budgets when search_budget_lost_impression_share is high vs. improving ad quality or bids when search_rank_lost_impression_share is high).
4. Do Not Attempt Unauthorized Local Probing: Do not attempt to run shell commands, custom Python scripts, local SQL/database queries, or direct RPC CLI utilities to access account data. These commands lack credentials and API access and will fail.

1. Conversion and Conversion Value Loss

When conversions or conversion value suddenly decline, use the following steps to diagnose the issue.

Steps:

1. Discover Fields: Use get_resource_metadata with resource campaign or ad_group to ensure you have the correct field names.

2. Query Performance: Use search to retrieve performance data.

   • Resource: campaign or ad_group
   • Fields: Include campaign.name, metrics.conversions, metrics.conversions_value, metrics.cost_micros.
   • Segments: To isolate the loss, include segments like segments.date, segments.device, segments.conversion_action.
   • Conditions: Compare the period of decline with a previous period (e.g., segments.date >= '{start_date}').
   • Gotcha: metrics.cost_micros must be divided by 1,000,000 to get standard currency amounts.
   • Gotcha (conversion action segmentation): In GAQL, metrics.cost_micros cannot be selected when segmenting by segments.conversion_action. Query conversion action breakdowns separately without metrics.cost_micros.

   Example GAQL Queries:

   1. To query performance and cost by date and device:

   SELECT
     campaign.name,
     metrics.conversions,
     metrics.conversions_value,
     metrics.cost_micros,
     segments.date,
     segments.device
   FROM campaign
   WHERE segments.date >= '{start_date}' AND segments.date <= '{end_date}'

   2. To query conversion breakdown by conversion action (without metrics.cost_micros):

   SELECT
     campaign.name,
     metrics.conversions,
     metrics.conversions_value,
     segments.date,
     segments.conversion_action
   FROM campaign
   WHERE segments.date >= '{start_date}' AND segments.date <= '{end_date}'

3. Analyze: Check if the loss is limited to certain devices (e.g., mobile vs desktop) or specific conversion actions.

4. Check Uploads: If using offline imports, query offline_conversion_upload_conversion_action_summary to verify upload pipeline health. If the query returns no results, report that no offline uploads exist for the account and proceed.

   Example GAQL Query: To check upload pipeline health for a customer account {customer_id}:

   SELECT
     offline_conversion_upload_conversion_action_summary.conversion_action_name,
     offline_conversion_upload_conversion_action_summary.successful_event_count,
     offline_conversion_upload_conversion_action_summary.total_event_count,
     offline_conversion_upload_conversion_action_summary.status
   FROM offline_conversion_upload_conversion_action_summary

2. Opportunities Lost (Impression Share)

To identify lost opportunities due to ad rank, bids, or budgets, analyze impression share metrics.

Steps:

1. Query Impression Share: Use search to retrieve impression share metrics.

   • Resource: campaign
   • Fields: Include campaign.name, metrics.search_impression_share, metrics.search_rank_lost_impression_share, metrics.search_budget_lost_impression_share.
   • Gotcha: Impression share values in the API are returned as decimals (e.g., 0.35 = 35%) or formatted strings (e.g., "< 0.10").

   Example GAQL Query: To query impression share metrics for a customer account {customer_id} between {start_date} and {end_date}:

   SELECT
     campaign.name,
     metrics.search_impression_share,
     metrics.search_rank_lost_impression_share,
     metrics.search_budget_lost_impression_share
   FROM campaign
   WHERE segments.date >= '{start_date}' AND segments.date <= '{end_date}'

2. Analyze:

   • High search_budget_lost_impression_share indicates opportunities lost due to limited budget.
   • High search_rank_lost_impression_share indicates opportunities lost due to low ad rank (bid or quality issues).

3. Low Lead Flow Diagnostics

When a user asks "why is my lead flow low these past few days?", follow this systematic approach.

Steps:

1. Confirm Drop: Query conversions segmented by date for the last few days vs the previous period.

2. Isolate Cause:

   • Check if Traffic (clicks, impressions) dropped.
   • Check if Conversion Rate (conversions/clicks) dropped.

   Example GAQL Query (Traffic and Conversion Trends): To query traffic and conversion rate metrics across campaigns segmented by date:

   SELECT
     campaign.id,
     campaign.name,
     segments.date,
     metrics.impressions,
     metrics.clicks,
     metrics.ctr,
     metrics.conversions,
     metrics.conversions_from_interactions_rate
   FROM campaign
   WHERE segments.date >= '{start_date}' AND segments.date <= '{end_date}'
   ORDER BY segments.date DESC

3. If Traffic Dropped: Check Impression Share metrics (see Workflow 2) to see if it's a budget or rank issue, or if search volume generally declined.

4. If Conversion Rate Dropped: Check breakdowns by segments.device or segments.conversion_action to see if a specific area is failing.

5. Check Changes: Query the change_event resource to see if any changes were made to bids, budgets, or targeting around the time the drop started.

   • Gotcha (change_event constraints): Queries to the change_event resource:
     • Must specify a LIMIT clause of less than or equal to 10000.
     • Must filter by date (change_event.change_date_time) within the past 30 days.
     • Cannot select performance metrics (e.g., metrics.* is not supported; only change_event attributes and allowed resource fields can be selected).

   Example GAQL Query: To query change events for a customer account {customer_id} between {start_date} and {end_date}:

   SELECT
     change_event.change_date_time,
     change_event.change_resource_name,
     change_event.resource_change_operation,
     change_event.changed_fields
   FROM change_event
   WHERE change_event.change_date_time >= '{start_date}' AND change_event.change_date_time <= '{end_date}'
   LIMIT 10000

4. Offline Upload Pipeline Diagnostics

When offline conversion uploads for a specific action (e.g., store-purchase) stop showing up or fail, use the following steps to diagnose the issue.

Steps:

1. Retrieve Client Accounts: If {customer_id} is not provided, follow Identifying Active Client Accounts to retrieve active client customer accounts.

2. Verify Pipeline Health: Query offline_conversion_upload_conversion_action_summary for the active client account.

   • Fields: Include offline_conversion_upload_conversion_action_summary.conversion_action_name, offline_conversion_upload_conversion_action_summary.successful_event_count, offline_conversion_upload_conversion_action_summary.total_event_count, and offline_conversion_upload_conversion_action_summary.status.

   Example GAQL Query:

   SELECT
     offline_conversion_upload_conversion_action_summary.conversion_action_name,
     offline_conversion_upload_conversion_action_summary.successful_event_count,
     offline_conversion_upload_conversion_action_summary.total_event_count,
     offline_conversion_upload_conversion_action_summary.status
   FROM offline_conversion_upload_conversion_action_summary

3. Analyze:

   • Gotcha: If the query to offline_conversion_upload_conversion_action_summary returns no results or is empty (indicating there are no offline conversion uploads configured or active for the customer account), immediately stop/break the diagnostic workflow. Report directly to the user that no offline conversion upload data or summaries exist in the accessible account(s), rather than retrying or attempting to generate custom scripts.
   • If results are returned, verify the upload success rate by comparing successful_event_count with total_event_count. Check the status field to diagnose failures.