Google Ads Account Diagnostics Skill

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

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.

Using the MCP Tools Directly

To retrieve information and run queries, you must call the search tool on the MCP server directly (with arguments like customer_id, fields, resource, and conditions). Do not write or execute custom Python scripts or use the Google Ads client library to query the API, as they will fail authentication inside the evaluation sandbox.

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.

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

   SELECT
     campaign.name,
     metrics.conversions,
     metrics.conversions_value,
     metrics.cost_micros,
     segments.date,
     segments.device,
     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.

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, first call the list_accessible_customers (or customers_list_accessible_customers) tool to retrieve the customer resource names/IDs you have access to. Then, query the customer_client resource to find active client customer accounts, ensuring you filter out manager accounts and deactivated/canceled accounts to avoid query errors.

   Example GAQL Query:

   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

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.