Overview

Tool Name

dbt_building_tools

Purpose

DBT Building Tools accelerate dbt development by scaffolding projects, preparing specialized dbt agents, and running work from mission tasks. This enables fast, repeatable patterns for data modeling at scale.

Key Features & Functions

Project initialization

Create standardized dbt project structures and configs.

Specialized agent creation

Spin up dbt-focused data agents with the right tool access.

Task-driven execution

Run dbt tasks from todos to keep work coordinated and auditable.
Note: Exact argument names may vary by deployment. The tables below describe typical fields and formats.

Input Parameters for Each Function

dbt_building_tools_init_project

Parameters
NameDefinitionFormat
project_nameName for the new dbt project.String
adapterTarget adapter (e.g., snowflake, bigquery, redshift).String
target_schemaDefault schema for models.String
profiles_sourceLocation/reference to dbt profiles for credentials.String/JSON
seed_demo_dataIf true, populate sample structures or seeds.Boolean

dbt_building_tools_create_bot

Parameters
NameDefinitionFormat
bot_nameHuman-friendly name for the dbt agent.String
available_toolsComma-separated tool groups to grant (e.g., dbt_action, git_action, snowflake_tools).String
slack_access_openWhether Slack access is open to all users initially.Boolean
runner_idOptional runner binding for execution.String

dbt_building_tools_run_todo

Parameters
NameDefinitionFormat
todo_idThe mission todo to execute (drives context).String
commanddbt operation to perform (e.g., build, run, test, seed).String
modelsOptional selector(s).String or List
varsRuntime variables to pass to dbt.JSON
full_refreshWhether to perform a full refresh for incremental models.Boolean

Use Cases

  • Initialize a new dbt project for a domain team
  • Create a dbt-specialized agent to own model build/test tasks
  • Run dbt build/test as part of a mission, tied to todos and dependencies

Workflow/How It Works

  1. Initialize project scaffolding for consistent structure.
  2. Create a dbt agent with appropriate tool access (dbt_action, git_action, snowflake_tools).
  3. Wire mission todos to dbt runs; record results and artifacts.
  4. Iterate with PRs and CI-like patterns using Git Action Tools.
Store build artifacts and docs so runs stay auditable and easily reproducible.

Integration Relevance

  • DBT Action Tools for build/test/compile operations
  • Git Action Tools for versioning, diffs, and history
  • Project Manager Tools to orchestrate todo execution
  • Snowflake Tools for stage access and Snowpark usage when needed

Configuration Details

  • Standardize adapter configuration and profiles handling.
  • Store run outputs and docs as artifacts for reproducibility.

Limitations or Notes

  • Parameter names can differ by implementation. Validate against your environment’s schema before automating.
  • Long-running builds should be scheduled with adequate warehouse capacity.

Output

  • Init: Project scaffolding details (paths, profiles references)
  • Create Data Agent: Bot id and capabilities summary
  • Run Todo: Execution log, status, and artifacts (tests, docs, manifests)