> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getcanonic.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Guide: Jaffle Shop (dbt + MetricFlow)

> The classic dbt Jaffle Shop dataset, with MetricFlow semantic models as modeling-tier evidence.

A canonic project backed by the classic [dbt Jaffle Shop](https://github.com/dbt-labs/jaffle-shop) dataset, demonstrating canonic's full Phase 1 feature set with a dbt manifest that includes **MetricFlow semantic models and metrics**: not just table/column evidence.

<Info>Full source: [`examples/jaffle-shop/`](https://github.com/mischuh/canonic/tree/main/examples/jaffle-shop)</Info>

## Schema

```
customers ──< orders ──< order_items >── products
                 │
               stores
```

| Table         | Rows | Description                              |
| ------------- | ---- | ---------------------------------------- |
| `customers`   | 20   | Individual and business accounts         |
| `stores`      | 5    | Physical Jaffle Shop locations           |
| `products`    | 10   | Jaffles and beverages                    |
| `orders`      | 25   | One row per order with payment breakdown |
| `order_items` | 37   | One row per line item                    |

## Quickstart

```bash theme={null}
cd examples/jaffle-shop

canonic ingest --bootstrap                              # introspects DuckDB + loads dbt manifest as modeling-tier evidence
canonic query --metrics revenue --dimensions store_id    # revenue by store
canonic mcp start                                        # expose metrics to any MCP-compatible agent
```

No LLM is required for the deterministic bootstrap. Set `CANONIC_LLM_API_KEY` and configure `llm:` in `canonic.yaml` to enable grain-drafting for low-confidence proposals.

## What canonic extracts from the dbt manifest

The `jaffle_dbt` connection parses `manifest.json` (schema v11, dbt 1.7, MetricFlow) as modeling-tier evidence, which ranks higher than live DuckDB introspection during reconciliation. From it, canonic extracts:

* **5 model nodes** → `RelationSchema` with named columns, types, primary keys, and foreign-key paths.
* **2 semantic models** → entity (grain), foreign-join paths, named measure and dimension definitions.
* **3 metrics** → `revenue`, `order_count`, `units_sold`.

Because MetricFlow's semantic models name measures explicitly, the resulting semantic sources in `semantics/jaffle_duckdb/` carry business-meaningful measure names (`revenue`, `order_count`) rather than generic inferred ones (`total_amount`, `row_count`); the clearest illustration in these examples of why [modeling-tier evidence outranks raw introspection](/concepts/connectors#schema-acquisition-ladder).

## Regenerating the artifacts

```bash theme={null}
bash scripts/build.sh
```

Clones the upstream `dbt-labs/jaffle-shop`, runs `dbt build` with `dbt-duckdb`, and copies the resulting database and manifest back into the example directory.