Agent context
BigCommerce models
2 SQL models that turn the raw BigCommerce 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
- 8Staging
- 2Core
- 2Analytics
Staging cleans each raw table once, core holds the logic, and analytics is the table dashboards and agents read.
- Product sales over timeThe sales equation per order line, with product refunds on the day they were made.grain: day x store x order x order line x row type (SALE or RETURN) · 6 raw tables
- Sales over timeThe sales equation per order per day, with refunds on the day they were made.grain: day x store x order x row type · 5 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 BigCommerce's own reports first. Each model's notes say what the synced data can and can't support.
Connect BigCommerce.
Your agents start with context.
Sync BigCommerce to your warehouse, deploy these models, and query them from Claude, Cursor or ChatGPT over MCP. Free for 14 days, no credit card.