DBT CLI MCP Server

What is DBT CLI MCP Server? DBT CLI MCP Server is 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. How to use DBT CLI MCP Server? To use the DBT CLI MCP Server, clone the repository, set up a Python virtual environment, install dependencies, and run dbt commands through the command-line interface or directly using Python. Key features of DBT CLI MCP Server? 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 Use cases of DBT CLI MCP Server? Running dbt models in a standardized way. Testing dbt projects efficiently. Managing dbt project configurations through environment variables. FAQ from DBT CLI MCP Server? What are the prerequisites for using DBT CLI MCP Server? You need Python 3.10 or higher, the uv tool for Python environment management, and dbt CLI installed. How do I run dbt commands? Use the command-line interface provided by the server, e.g., dbt-mcp run --models customers --project-dir /path/to/project. Can I configure the dbt executable path? Yes, you can set the --dbt-path option to specify the path to the dbt executable.

As an MCP (Model Context Protocol) server, DBT CLI MCP Server enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use the DBT CLI MCP Server, clone the repository, set up a Python virtual environment, install dependencies, and run dbt commands through the command-line interface or directly using Python. Key features of DBT CLI MCP Server? 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 Use cases of DBT CLI MCP Server? Running dbt models in a standardized way. Testing dbt projects efficiently. Managing dbt project configurations through environment variables. FAQ from DBT CLI MCP Server? What are the prerequisites for using DBT CLI MCP Server? You need Python 3.10 or higher, the uv tool for Python environment management, and dbt CLI installed. How do I run dbt commands? Use the command-line interface provided by the server, e.g., dbt-mcp run --models customers --project-dir /path/to/project. Can I configure the dbt executable path? Yes, you can set the --dbt-path option to specify the path to the dbt executable.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.