dbt CLI MCP server

DBT CLI MCP Server is a tool that enables AI coding agents to interact with dbt projects through standardized Model Context Protocol (MCP) tools. It wraps the dbt CLI, allowing you to execute dbt commands, manage environment variables, and configure various dbt operations.

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

You can interact with dbt directly through the command line:

# 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

Configure the server 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:

{
  "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
      }
    }
  }
}

Available Tools

The server provides these MCP tools:

• dbt_run: Run dbt models (requires absolute project_dir)
• dbt_test: Run dbt tests (requires absolute project_dir)
• dbt_ls: List dbt resources (requires absolute project_dir)
• dbt_compile: Compile dbt models (requires absolute project_dir)
• dbt_debug: Debug dbt project setup (requires absolute project_dir)
• dbt_deps: Install dbt package dependencies (requires absolute project_dir)
• dbt_seed: Load CSV files as seed data (requires absolute project_dir)
• dbt_show: Preview model results (requires absolute project_dir)

Important: Absolute Project Path Required

When using any tool from this MCP server, you MUST specify the FULL ABSOLUTE PATH to your dbt project directory with the project_dir parameter.

// ❌ INCORRECT - Will NOT work
{
  "project_dir": "."
}

// ✅ CORRECT - Will work
{
  "project_dir": "/Users/username/path/to/your/dbt/project"
}

dbt Profiles Configuration

When using the dbt MCP tools, it's important to understand how dbt profiles are handled:

1. The project_dir parameter MUST be an absolute path that points to a directory containing both:

   • A valid dbt_project.yml file
   • A valid profiles.yml file with the profile referenced in the project

2. The MCP server automatically sets the DBT_PROFILES_DIR environment variable to the absolute path of the directory specified in project_dir.

3. If you encounter a "Could not find profile named 'X'" error, it means either:

   • The profiles.yml file is missing from the project directory
   • The profiles.yml file doesn't contain the profile referenced in dbt_project.yml
   • You provided a relative path instead of an absolute path for project_dir

Example of a valid profiles.yml file:

jaffle_shop:  # This name must match the profile in dbt_project.yml
  target: dev
  outputs:
    dev:
      type: duckdb
      path: 'jaffle_shop.duckdb'
      threads: 24

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.