iflow-mcp_dbt-cli-mcp

# DBT CLI MCP Server A Model Context Protocol (MCP) server that wraps the dbt CLI tool, enabling AI coding agents to interact with dbt projects through standardized MCP tools. ## Features - Execute dbt commands through MCP tools - Support for all major dbt operations (run, test, compile, etc.) - Command-line interface for direct interaction - Environment variable management for dbt projects - Configurable dbt executable path - Flexible profiles.yml location configuration ## Installation ### Prerequisites - Python 3.10 or higher - `uv` tool for Python environment management - dbt CLI installed ### Setup ```bash # Clone the repository with submodules git clone --recurse-submodules https://github.com/yourusername/dbt-cli-mcp.git cd dbt-cli-mcp # If you already cloned without --recurse-submodules, initialize the submodule # git submodule update --init # Create and activate a virtual environment uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate # Install dependencies uv pip install -e . # For development, install development dependencies uv pip install -e ".[dev]" ``` ## Usage ### Command Line Interface The package provides a command-line interface for direct interaction with dbt: ```bash # Run dbt models dbt-mcp run --models customers --project-dir /path/to/project # Run dbt models with a custom profiles directory dbt-mcp run --models customers --project-dir /path/to/project --profiles-dir /path/to/profiles # List dbt resources dbt-mcp ls --resource-type model --output-format json # Run dbt tests dbt-mcp test --project-dir /path/to/project # Get help dbt-mcp --help dbt-mcp run --help ``` You can also use the module directly: ```bash python -m src.cli run --models customers --project-dir /path/to/project ``` ### Command Line Options - `--dbt-path`: Path to dbt executable (default: "dbt") - `--env-file`: Path to environment file (default: ".env") - `--log-level`: Logging level (default: "INFO") - `--profiles-dir`: Path to dir…