Configure settings

This page describes how to configure general settings for Mainframe Assessment Tool and modify default AI features for new assessments.

The modified settings don't apply to existing assessments. To use the modified settings, create a new assessment.

Configure general settings

To configure general settings for your assessment, follow these steps:

  1. Click the settingsSettings icon.

  2. To edit your preferences for Cloud Logging and Google Analytics, in the General settings section, do the following:

    • To turn off Logging, click the Enable Cloud Logging toggle to the off position. By default, this setting is enabled.

      For more information, see Cloud Logging documentation.

    • To turn off Google Analytics, click the Enable Google Analytics toggle to the off position. By default, Google Analytics is enabled. Changes to this setting only take effect after you reload the page.

  3. For Google Cloud project, enter the name for the Google Cloud project ID that you want to use for your assessment.

    To use the Google Cloud project where you created your Mainframe Assessment Tool instance, leave this field blank.

  4. For Serving tier, select a Gemini Enterprise Agent Platform serving tier. Default is Standard.

    The serving tier setting determines the inference and consumption model applied to Gemini model calls during mainframe assessments. For more information about Gemini Enterprise Agent Platform serving tiers, see PayGo.

  5. Click Save settings.

Configure default AI features for new assessments

Mainframe Assessment Tool lets you customize the default parameters for AI insights in your assessments. By default, AI insights are enabled.

To configure AI features in new assessments, follow these steps:

  1. Click the settingsSettings icon.

  2. To generate paragraph-level summaries for COBOL code, select Detailed summaries.

    By default, this option is selected when AI analysis is enabled.

  3. To generate test cases for the assessment specifications generated by Gemini, select Test cases.

    By default, this option is not selected when AI analysis is enabled.

  4. To generate output code samples suggesting translation from mainframe languages to Python, Java, C#, and SQL, select Code suggestions.

  5. For Tech stack hints, enter text that helps Gemini generate code for a specific tech stack. For example, enter Spring Boot and MySQL.

  6. Click Save settings.

Optional: Perform actions

This section describes how to perform actions, such as clearing the Gemini cache and verifying connectivity to Gemini.

To perform actions, follow these steps:

  1. Click the settingsSettings icon.

  2. To test your connectivity from Mainframe Assessment Tool to Google Cloud services, click Verify connectivity.

    If the connection is successful, you see a message similar to the following: Connectivity success.

  3. To clear the Gemini cache, click Clear model cache.

  4. To download your Mainframe Assessment Tool logs, click Download support bundle.

    The support bundle contains the Mainframe Assessment Tool logs as a zip file, which you can share with Google Cloud support to troubleshoot issues.

Disable AI insights for assessments

You can disable AI analysis to prevent AI insights from appearing on the Assessments page.

To disable AI analysis for assessments, follow these steps:

  1. In the Default AI features in new assessments section, clear Enable AI insights.

  2. Click Save settings.

Track Agent Platform costs using custom metadata labels

Mainframe Assessment Tool automatically adds custom metadata labels to all Agent Platform requests to help you track and analyze costs. In your billing report, you can use these labels to filter costs to better understand and optimize your Agent Platform usage. The costs are updated in the billing report within 24 hours of usage, but in some cases can take longer.

Filter costs in your billing report with the following labels:

  • mat-version: version of Mainframe Assessment Tool.
  • mat-host: host environment of the Mainframe Assessment Tool instance. For example, Compute Engine VM or Google Kubernetes Engine.
  • mat-action-type: type of action performed by Mainframe Assessment Tool.
  • mat-schema: type of asset processed. For example, COBOL or JCL.
  • mat-target: deployment target of the Mainframe Assessment Tool instance.
  • mat-run-id: unique ID of the assessment.

To track usage by using one or more of these filters in the billing report, see Use filters to refine data.

For more information about labels in Agent Platform, see Custom metadata labels.

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