dbt-setup

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.