What is the reason this setting causes a 413 Entity Too Large when requesting queue metrics for a 90-day window? I am running a Jupyter notebook in our Singapore BPO environment to pull historical queue performance data using the Genesys Cloud Python SDK. The goal is to analyze call volume trends over the last quarter, but the API rejects the request before returning any data. I have verified that the OAuth token is valid and has the correct analytics:report:view scope. The issue arises specifically when the interval is set to P1D for a 90-day range. I suspect the payload size or the number of data points exceeds the server’s limit, but the documentation is vague on the exact threshold for range queries. My code snippet looks like this: from genesyscloud import analytics_api; config = genesyscloud.Configuration(); api_instance = analytics_api.AnalyticsApi(genesyscloud.ApiClient(config)); queue_id = 'abc123-def456'; start_time = '2023-01-01T00:00:00.000Z'; end_time = '2023-03-31T23:59:59.999Z'; body = analytics_api.QueueRq(interval='P1D', group_by='time'); response = api_instance.post_analytics_queue_ranges(body=body, queue_id=queue_id, start_time=start_time, end_time=end_time);. The error response is 413: Request Entity Too Large. I have tried reducing the interval to P3D which works, but I need daily granularity. Is there a way to split the query programmatically in Python to avoid this error, or is there a specific parameter to paginate the results? I am using pandas to process the resulting DataFrame, so I can handle multiple API calls if necessary, but I need a robust way to chunk the date range without hitting this limit repeatedly. The current approach of manually splitting dates is not scalable for automated reporting. Any advice on handling large date ranges in the Analytics API would be appreciated.