一个模型上下文协议(MCP)服务器可以包接dbt CLI工具,使AI编码代理通过标准化的MCP工具与dbt项目交互.

□ 特性

• 通过 MCP 工具执行 dbt 命令
• 支持所有主要 dbt 操作(运行、测试、编译等)
• 直接互动的命令线接口
• dbt项目的环境可变管理
• 可配置 dbt 可执行路径
• 灵活配置.yml 位置配置

□ 安装

· 先决条件

• Python 3.10 或 更高级
• [T0]用于Python环境管理的工具
• 安装dbt CLI

QQ 设置

□ 使用量

QQ 命令行接口

该软件包为直接与 dbt 交互提供了命令行接口:

您也可以直接使用模块 :

QQ 命令行选项

• [[T1]: dbt 可执行文件的路径( 默认为 “dbt” )
• [[T2]: 环境文件路径( 默认 : “. env”)
• [[T3]: 日志级别( 默认为“ INFO ”)
• [[T4]: 包含 project.yml 文件的目录路径( 如果未指定默认为工程目录)

环境变量

服务器也可以使用环境变量配置:

• [[T5]: dbt 可执行文件的路径

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

# 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:

# 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:

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 directory containing profiles.yml file (defaults to project-dir if not specified)

Environment Variables

The server can also be configured using environment variables:

• DBT_PATH: Path to dbt executable
• ENV_FILE: Path to environment file
• LOG_LEVEL: Logging level
• DBT_PROFILES_DIR: Path to directory containing profiles.yml file

Using with MCP Clients

To use the server with an MCP client like Claude for Desktop, add it to the client’s configuration:

{
  "mcpServers": {
    "dbt": {
      "command": "uv",
      "args": ["--directory", "/path/to/dbt-cli-mcp", "run", "src/server.py"],
      "env": {
        "DBT_PATH": "/absolute/path/to/dbt",
        "ENV_FILE": ".env"
        // You can also set DBT_PROFILES_DIR here for a server-wide default
      }
    }
  }
}

⚠️ IMPORTANT: Absolute Project Path Required ⚠️