dbt-setup

name	dbt-setup
description	Scaffolds a complete dbt-fabric project for a Fabric Data Warehouse: generates profiles.yml, dbt_project.yml, a column-rich _sources.yml (with data types for every table column, ready for dbt contracts), requirements.txt, and .gitignore, then writes them to the local filesystem. Use when the user asks to "set up dbt", "scaffold a dbt project", "create dbt profile", "initialize dbt for my warehouse", "generate dbt sources", or "bootstrap dbt-fabric".
user-invocable	true

dbt Project Bootstrap for Fabric DW

Generates a complete dbt-fabric project scaffold using the fabric-dw MCP tools and writes all files to the local filesystem.

Inputs

Gather these from the user (via $ARGUMENTS or natural language) before starting:

• workspace: workspace name or GUID
• warehouse: Fabric Data Warehouse name or GUID
• project_name: desired dbt project name (default: warehouse name, lowercased, spaces replaced with underscores)
• schema: default dbt schema (default: dbo)
• authentication: default (DefaultAzureCredential), sp (service principal), or interactive (default: default)
• with_sources: whether to generate a column-rich _sources.yml (default: true, recommended)

Workflow

Step 1 - Resolve the warehouse connection

Call get_warehouse with the workspace and warehouse name. This returns the warehouse metadata including the connection string used in the dbt profile.

Step 2 - Confirm connectivity and list objects

Call list_schemas and list_tables to confirm that the warehouse is reachable and to identify the schemas and tables that will appear as dbt sources.

Show the user a summary: number of schemas found, number of tables found.

Step 3 - Generate the dbt scaffold

Call generate_dbt_profile with:

• workspace, warehouse, project_name, schema, authentication as provided
• with_sources=true (unless the user explicitly opted out)

When with_sources=true, the tool performs a bulk column fetch and emits a models/staging/_sources.yml that already includes each table's columns: block with name and formatted data_type for every column. This output is ready for dbt contract enforcement and column-level documentation without any manual assembly.

Step 4 - Write files to the local filesystem

Write all file contents returned by generate_dbt_profile to the user's working directory. The expected set of files:

Path	Purpose
profiles.yml	dbt connection profile
dbt_project.yml	dbt project definition
models/staging/_sources.yml	Source definitions with column types (when with_sources=true)
requirements.txt	Python package dependencies (dbt-fabric)
.gitignore	Excludes secrets and build artefacts

Before writing, check whether any of these files already exist and confirm with the user before overwriting.

Step 5 - Provide next steps

After writing the files, give the user the following instructions:

# Install dependencies
pip install -r requirements.txt

# Verify the connection
dbt debug

# Run your first dbt project
dbt run

Also remind the user to:

1. Review profiles.yml and fill in any {{ env_var(...) }} placeholders (service-principal credentials are never written as literals; they are templated).
2. Check that environment variables (AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET) are set before running dbt debug if using service-principal auth.

Guardrails

• Service-principal auth: when authentication=sp, the generated profiles.yml uses {{ env_var(...) }} placeholders for tenant ID, client ID, and client secret. Never write literal credential values. Remind the user that these must be set in environment before committing profiles.yml to version control.
• Overwrite protection: if any target file already exists, list them and ask the user to confirm before overwriting.
• Fabric auth only: dbt-fabric requires Entra (Azure AD) authentication. Warn the user if they ask about username/password auth. It is not supported by the dbt-fabric adapter.
• Column inspection: get_table_columns and get_view_columns are available for ad-hoc schema inspection (e.g., if the user wants to review a specific table before deciding which columns to expose), but explicit calls to those tools are not required; column data is built into generate_dbt_profile with with_sources=true.