dbt-toolbox 0.6.1

Productivity, warehous cost and QoL improvements for your dbt development

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A powerful CLI toolkit to supercharge your dbt development workflow

Built with the tools and technologies:

dbt-toolbox

A powerful CLI toolkit that supercharges your dbt development workflow with intelligent caching, dependency analysis, and enhanced documentation generation.

🚀 What Makes dbt-toolbox Amazing

Smart Caching & Performance

• Lightning-fast model parsing with intelligent cache invalidation
• Persistent Jinja environment caching for instant macro resolution
• Dependency graph caching for rapid upstream/downstream analysis

Enhanced dbt Commands

• dt build - Drop-in replacement for dbt build with enhanced output and performance
• dt run - Drop-in replacement for dbt run with smart execution and caching
• Target-specific options for environment control
• Intelligent pre/post processing hooks

LLM Ready via MCP server

• Contains mcp server to make your LLM powered dbt development even better.
• Tools support the most common dbt-toolbox featuers.

Intelligent Documentation

• dt docs - YAML documentation generator with smart column inheritance
• Automatically inherits descriptions from upstream models and macros
• Tracks column changes (additions, removals, reordering) between runs
• One-click clipboard integration

Dependency Intelligence

• Lightweight DAG implementation for model and macro relationships
• Efficient upstream/downstream traversal
• Node type tracking and statistics
• Perfect for impact analysis and refactoring

Configuration

• Multi-source settings hierarchy (env vars > TOML > dbt profiles > defaults)
• Dynamic dbt profile and target integration
• Source tracking for all configuration values

🛠️ Installation

# Using uv
uv add dbt-toolbox

# Or install with pip
pip install dbt-toolbox

# Install with MCP server support
uv add "dbt-toolbox[mcp]"

⚡ Quick Start

# Initialize and explore your project
dt settings                    # View all configuration

# Enhanced dbt commands with caching
dt build                      # Build with intelligent caching
dt run --model +my_model+     # Support for most dbt selection syntax
dt build --target prod        # Build against production target

# Analyze cache and dependencies
dt analyze                    # Analyze all models
dt analyze --model customers+ # Analyze specific model selection

# Generate documentation YAML
dt docs --model customers     # Generate docs for specific model
dt docs -m orders --clipboard # Copy to clipboard

📋 Core Commands

Command	Description
dt build	Enhanced dbt build with caching and better output
dt run	Enhanced dbt run with intelligent execution and caching
dt docs	Generate YAML documentation with smart inheritance
dt analyze	Analyze cache state and model dependencies without execution
dt clean	Clear all cached data with detailed reporting
dt settings	Inspect configuration from all sources

MCP Server Integration

dbt-toolbox can also run as an MCP (Model Context Protocol) server, enabling integration with external tools and AI assistants like Claude Code.

Key Features:

• Expose dbt-toolbox functionality through standardized MCP protocol
• Same intelligent caching and validation as CLI commands
• Perfect for AI-assisted dbt development workflows

Quick Setup:

# Install with MCP support
uv add "dbt-toolbox[mcp]"

# Available MCP tools:
# - analyze_models: Validate model references and column lineage
# - build_models: Build models with intelligent execution

For detailed MCP server setup and usage, see MCP.md.

🏗️ Key Features

Intelligent Caching System

dbt-toolbox caches parsed models, macros, and Jinja environments in .dbt_toolbox/ directory with smart invalidation based on file changes and project configuration.

Smart Execution & Lineage Validation

• Only executes models that actually need rebuilding based on dependency analysis
• Optional lineage validation to catch broken references before execution
• Configurable model validation ignore lists for legacy models
• Cache validity controls for optimal performance

Dependency Graph Analysis

Lightweight DAG implementation provides efficient model relationship tracking:

• Upstream/downstream dependency resolution
• Node type classification (models, macros, sources)
• Impact analysis for refactoring

Enhanced CLI Experience

• Colored output with progress indicators
• Target-specific execution (--target option)
• Command shadowing for seamless dbt integration
• Comprehensive error handling and reporting

Smart Documentation Generation

The dt docs command intelligently inherits column descriptions from:

• Upstream model columns with matching names
• Macro parameters that reference the columns
• Existing schema.yml documentation

⚙️ Configuration

dbt-toolbox supports configuration through multiple sources with the following precedence:

1. Environment Variables (highest priority)
2. TOML Configuration (pyproject.toml)
3. dbt Profiles (for SQL dialect)
4. Auto-detection (for project paths)
5. Defaults (lowest priority)

Key Configuration Options

For an exhaustive list see CLI.md

Setting	Description	Default
dbt_project_dir	Path to dbt project	Auto-detected
cache_path	Cache directory location	.dbt_toolbox
cache_validity_minutes	Cache validity duration	1440 (24 hours)
enforce_lineage_validation	Enable lineage validation	true
models_ignore_validation	Models to skip validation	[]
debug	Enable debug logging	false
skip_placeholder	Skip placeholder descriptions	false
placeholder_description	Custom placeholder text	"TODO: PLACEHOLDER"

Configuration Examples

Environment Variables:

export DBT_TOOLBOX_ENFORCE_LINEAGE_VALIDATION=false
export DBT_TOOLBOX_MODELS_IGNORE_VALIDATION="legacy_model,staging_temp"
export DBT_TOOLBOX_CACHE_VALIDITY_MINUTES=720 # Default=1440

TOML Configuration:

[tool.dbt_toolbox]
enforce_lineage_validation = false
models_ignore_validation = ["legacy_model", "staging_temp"]
cache_validity_minutes = 720 # Default=1440

📚 Documentation

• CLI Reference - Detailed command documentation and examples
• MCP Server Guide - MCP server setup and integration
• Contributing Guide - Development setup and guidelines

🧪 Testing Integration

dbt-toolbox includes a testing module for your dbt projects:

from dbt_toolbox.testing import check_column_documentation

def test_model_documentation():
    """Ensure all model columns are documented."""
    result = check_column_documentation()
    if result:
        pytest.fail(result)

⭐ Roadmap

• dt docs: Automatic yaml docs generation.
• Smart model selection: Smart caching and model selection for optimized executions.
• MCP Server: Publish commands via MCP server.
• Expand testing stack: Build out the dbt_toolbox.testing stack.
• dt test Command, with test parsing and caching.

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for development setup, coding standards, and contribution guidelines.

📄 License

MIT License - Feel free to use this project in your own work.

🙏 Acknowledgments

Built with modern Python tooling:

• Typer for the CLI framework
• SQLGlot for SQL parsing and optimization
• Jinja2 for template processing
• yamlium for YAML manipulation and generation
• uv for dependency management