Dbt MCP

The dbt MCP Server is a service that provides tools for interacting with dbt. It supports features such as dbt CLI, Semantic Layer, and Discovery API, and can be integrated with multiple clients through configuration.

Developer tools Database #dbt tool #Data modeling #Cloud service #API integration .Python

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update time : 2025-04-28

What is the dbt MCP Server?

The dbt MCP Server provides a way to interact with dbt tools (such as dbt Core and dbt Cloud) through the Model Context Protocol (MCP). It supports multiple tools and features, aiming to simplify the data analysis workflow.

How to use the dbt MCP Server?

After installation, you can configure client tools to connect to the dbt MCP Server and perform related tasks, such as model building and query analysis.

Applicable scenarios

Suitable for teams that need to efficiently manage and operate data warehouses, especially those organizations that rely on dbt.

Main features

dbt CLI toolSupports dbt CLI commands, such as building, compiling, and testing models.

Semantic LayerAllows querying and managing metrics and dimensions defined in dbt.

Discovery APIGets detailed information about models and data sources.

Remote tool supportEnables more flexible data operations through a remote environment.

Advantages and limitations

Advantages

Simplifies the management and maintenance of data pipelines.

Improves data development efficiency.

Supports multi - platform client integration.

Limitations

Requires a certain technical background for configuration and use.

Some advanced features may require specific permissions.

How to use

Download and install the dbt MCP Server

Run the installation script to quickly set up the server.

Configure the client

Configure client tools according to the instructions to connect to the dbt MCP Server.

Start using

Execute common dbt commands, such as building and running models.

Usage examples

Build and run modelsUse the dbt build and dbt run commands to build and run models.

Query metricsUse the Semantic Layer to query metrics.

Frequently Asked Questions

How to install the dbt MCP Server?

Do special permissions need to be used?

How to uninstall the dbt MCP Server?

Related resources

dbt official documentation

The official dbt documentation, containing detailed guides.

dbt MCP Server GitHub repository

The source code of the dbt MCP Server on GitHub.

dbt community forum

The dbt user community, where you can ask questions and exchange experiences.

🚀 dbt MCP Server Guide

MCP (Model Command Protocol) is a technical protocol for managing and operating dbt projects. This document details how to install, configure, and use the dbt MCP server, as well as how to integrate it with different tools.

🚀 Quick Start

✨ Features

MCP (Model Command Protocol) is a technical protocol designed for the management and operation of dbt projects.

📦 Installation

Architecture

The dbt MCP server is an independent program that can run alongside the dbt CLI and other tools. Here's its core architecture:

Property	Details
dbt CLI	A command - line interface for executing modeling, testing, and other database operations.
Semantic Layer	Provides the ability to query metrics and entities.
Discovery	Used to obtain detailed information about models and datasets.

Installation Steps

Download and install Python.
Clone the dbt MCP repository using the following command:

git clone https://github.com/dbt-labs/dbt-mcp.git

Install the dependencies:

pip install -r requirements.txt

📚 Documentation

Configuration

Basic Configuration

Add the following content to the dbt_mcp/config.yml file:

server:
  host: localhost
  port: 8000

Advanced Configuration

Environment Variables

Set the following environment variables:

DBT_PROFILE: Specify the dbt configuration file.
DBT_PROJECT_DIR: Specify the dbt project directory.

Tools

dbt CLI

Here are the commonly used dbt commands:

Command	Description
build	Execute models, tests, snapshots, and seed files.
compile	Generate executable SQL without running them.
docs	Generate project documentation.
ls	List resources in the project, such as models and tests.
parse	Parse and validate the syntax of project files.
run	Execute models to generate target objects in the database.
test	Run tests to verify the integrity of data and models.

⚠️ Important Note

Allowing clients to use dbt commands through this MCP tool may modify your data models, sources, and database objects. Ensure that you only do this when you trust the client and understand the potential impacts.

Semantic Layer

Here are the commonly used commands for the Semantic Layer:

Command	Description
list_metrics	Get all defined metrics.
get_dimensions	Get the dimensions associated with a specified metric.
get_entities	Get the entities associated with a specified metric.
query_metrics	Query metrics, supporting grouping, sorting, filtering, and limiting.

Discovery

Here are the commonly used commands for Discovery:

Command	Description
get_mart_models	Get all mart models.
get_all_models	Get all models.
get_model_details	Get detailed information about a specific model.
get_model_parents	Get the parent models of a specific model.