Dbt Cli MCP
A Model Context Protocol (MCP) server based on the dbt CLI that provides a standardized interface for AI coding agents to operate dbt projects and supports the execution of all major dbt commands.
rating : 2.5 points
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What is the DBT CLI MCP Server?
The DBT CLI MCP Server is middleware that bridges the dbt CLI tool and AI coding agents. Through the standardized Model Context Protocol (MCP) tool, it allows AI assistants to directly operate and manage dbt projects. It encapsulates various functions of dbt and provides a unified interface for AI tools to call.How to use the DBT CLI MCP Server?
You can call it directly through the command line or configure it to run as a background service in an MCP client (such as Claude for Desktop). The main steps include installing the Python environment, configuring the dbt path, setting the project directory, and then you can interact with dbt through the standard MCP protocol.Applicable scenarios
Suitable for scenarios where dbt operations need to be integrated into AI workflows, such as: - AI assistants automatically execute data model tests - Obtain the dbt project structure during intelligent code completion - Schedule dbt tasks in automated data pipelinesMain features
dbt CLI encapsulationFully supports all core dbt commands (run/test/compile, etc.) and is called through a standardized interface
MCP protocol integrationComplies with the Model Context Protocol standard and can be seamlessly integrated with various AI development tools
Flexible configurationSupports custom dbt paths, environment variable management, and multi - project configuration
Multi - format outputSupports structured output formats such as JSON for easy program parsing and processing
Advantages and limitations
Advantages
Standardized interface: Unifies the calling methods of different dbt versions
AI - friendly: Outputs structured data for easy processing by AI tools
Environment isolation: Each project can be independently configured with environment variables
Cross - platform: Supports various operating system environments
Limitations
The project directory must be configured using an absolute path
Depends on the local dbt CLI environment
Basic knowledge of dbt is required for the initial configuration
Additional development may be required for advanced dbt functions
How to use
Environment preparation
Ensure that Python 3.10+ and the dbt CLI are installed. It is recommended to use uv to manage the Python environment.
Install the service
Clone the repository and install the dependencies (including development dependencies).
Basic usage
Call dbt commands directly through the command line.
MCP client integration
Configure it in the server list of an MCP client (such as Claude).
Usage examples
AI - assisted model developmentThe AI assistant obtains the project structure through MCP to help developers write correct model references.
Automated test pipelineExecute dbt tests through the MCP server in the CI/CD process.
Interactive data analysisCompile SQL models immediately and preview the results.
Frequently Asked Questions
Why must an absolute path be used?
How to solve the 'Could not find profile' error?
Can multiple dbt projects be managed simultaneously?
Which dbt adapters are supported?
Related resources
Official dbt documentation
dbt core functions and usage guide
MCP protocol specification
Model Context Protocol technical specification
Example project repository
An example dbt project for testing
Installation video tutorial
DBT CLI MCP Server installation and configuration demonstration
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