Configures a Marketing Mix Modeling (MMM) study for a project.
**What is MMM?**
Marketing Mix Modeling measures the real contribution of each marketing channel (Google Ads, Meta, etc.) on a KPI (leads, revenue, conversions), accounting for external factors (seasonality, holidays, promotions).
**Recommended workflow:**
1. Use get_schema_context to discover the project's tables/columns
2. Generate input SQL queries (KPI, channels, exogenous variables)
3. **Validate each query before calling setup_mmm:** Use execute_query to run a COUNT(*) wrapper on each input query (e.g., SELECT COUNT(*) FROM (<query>)). If any query returns 0 rows, do NOT include it in setup_mmm — warn the user that the data source is empty and ask whether to proceed without it or fix the query.
4. Call setup_mmm with the validated SQL queries — the study is automatically launched after setup
5. Do NOT call run_mmm after setup_mmm: the first run is triggered automatically
**Important:** run_mmm is only needed to RE-RUN an existing study later, not after initial setup.
**Input queries format:**
Each query must return a "time" column (DATE) and the requested metrics.
- role="kpi": a "kpi" column (the target KPI)
- role="channel": "spend" and "impressions" columns + channel_name
- role="exogenous": columns named after the exogenous variables + columns[]
**Granularity**: "weekly" is recommended (MMM standard). SQL should aggregate by week.
**Important**: Adapt the SQL dialect to the project's data warehouse type (BigQuery, Snowflake, Redshift).