Agent context
Iterable models
2 SQL models that turn the raw Iterable tables into the reports people ask about. Deploy them in Weld and your agent queries the definitions behind every number.
How the set is built
- 7Raw tables
- 7Staging
- 2Core
- 2Analytics
Staging cleans each raw table once, core holds the logic, and analytics is the table dashboards and agents read.
- Email campaign performanceOne row per email campaign with its whole-life numbers and rates: the headline table in Iterable's Campaign Analytics for email.grain: one row per campaign (email campaigns, plus any campaign id that has email sends) · 7 raw tables
- Email performance dailyEmail sends, opens, clicks, bounces, complaints and unsubscribes per campaign per day: the email metrics in Iterable's Campaign Analytics, as a daily series.grain: one row per day (UTC, event time) x campaign · 7 raw tables
- Weld or dbt
- The same SQL in both dialects. Sync the folder with GitHub Sync, or copy the dbt models into your project.
- Check before you rely on it
- Reconcile a month against Iterable's own reports first. Each model's notes say what the synced data can and can't support.
Connect Iterable.
Your agents start with context.
Sync Iterable to your warehouse, deploy these models, and query them from Claude, Cursor or ChatGPT over MCP. Free for 14 days, no credit card.