Docs / Capability guide
Audiences, IP lists, dayparts and frequency caps
Define who can receive an ad and test which requests match.
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When to use this example
When a campaign reaches the wrong audience or returns no fill, test one targeting condition at a time. A matching request and a request that differs in one field make the rule easier to check.
What the example gives you
The serving probe sends your saved request JSON and summarizes the response without firing tracking events. Compare those responses with eligibility diagnostics; frequency-cap checks also need recorded events from your player or application.
Before you start
A sandbox line item and synthetic targeting inputs; permission to create audiences and publish IP lists.
Run the example
Extract the archive and follow README.md for setup, commands, and expected results. Start with serving_probe.py. Local tests use sample data.
Python tools accept --help. To connect to your workspace, supply the deployment URLs, credentials and resource IDs required by the tool.
Connect it to your application
Create an audience from supported geo, device, content, app or custom-key dimensions. Combined dimensions use AND semantics. Attach it to the intended line item, creative, placement, route or deal using that resource's schema.
Import IP-list entries in chunks and publish the list before attaching it. Configure daypart windows in the documented time zone; an empty window is unrestricted. Use valid test identities when checking frequency caps.
Save two request JSON files: one matching the audience and one failing a single condition. Probe each and compare eligibility diagnostics. Keep COPPA and consent restrictions when adding custom audience signals.
Check the result
The negative case must be excluded for the expected reason. To verify frequency caps, use an identity and send events through their full lifecycle. The serving probe does not send beacons, so it cannot check whether an impression counts toward a cap.