dbt MCP Server

This MCP (Model Context Protocol) server provides tools to interact with dbt. Read this blog to learn more.

Architecture

Setup

1. Clone the repository:

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

2. Install uv

3. Install Task

4. Run task install

5. Configure environment variables:

cp .env.example .env

Then edit .env with your specific environment variables (see the Configuration section of the README.md).

Configuration

The MCP server takes the following environment variable configuration:

Tool Groups

Name	Default	Description
DISABLE_DBT_CLI	false	Set this to true to disable dbt Core and dbt Cloud CLI MCP tools
DISABLE_SEMANTIC_LAYER	false	Set this to true to disable dbt Semantic Layer MCP objects
DISABLE_DISCOVERY	false	Set this to true to disable dbt Discovery API MCP objects
DISABLE_REMOTE	true	Set this to false to enable remote MCP objects

Configuration for Discovery and Semantic Layer

Name	Default	Description
DBT_HOST	cloud.getdbt.com	Your dbt Cloud instance hostname. This will look like an Access URL found here. If you are using Multi-cell, do not include the ACCOUNT_PREFIX here
MULTICELL_ACCOUNT_PREFIX	-	If you are using Multi-cell, set this to your ACCOUNT_PREFIX. If you are not using Multi-cell, do not set this environment variable. You can learn more here
DBT_TOKEN	-	Your personal access token or service token. Note Service token is required when using the Semantic Layer
DBT_PROD_ENV_ID	-	Your dbt Cloud production environment ID

Configuration for Remote Tools

Name	Description
DBT_DEV_ENV_ID	Your dbt Cloud development environment ID
DBT_USER_ID	Your dbt Cloud user ID

Configuration for dbt CLI

Name	Description
DBT_PROJECT_DIR	The path to where the repository of your dbt Project is hosted locally. This should look something like /Users/firstnamelastname/reponame
DBT_PATH	The path to your dbt Core or dbt Cloud CLI executable. You can find your dbt executable by running which dbt

Using with MCP Clients

After going through Installation, you can use your server with an MCP client.

This configuration will be added to the respective client's config file. Be sure to replace the sections within <>:

 {
  "mcpServers": {
    "dbt-mcp": {
      "command": "<path-to-mcp-executable>",
      "args": [
        "run",
        "<path-to-this-directory>/src/dbt_mcp/main.py"
      ]
    }
  }
}

<path-to-mcp-executable> depends on your OS:

• Linux & Mac: <path-to-this-directory>/.venv/bin/mcp
• PC: <path-to-this-directory>/.venv/Scripts/mcp

Claude Desktop

Follow these instructions to create the claude_desktop_config.json file and connect.

On Mac, you can find the Claude Desktop logs at ~/Library/Logs/Claude.

Cursor

1. Open the Cursor menu and select Settings → Cursor Settings → MCP
2. Click "Add new global MCP server"
3. Add the config from above to the provided mcp.json file
4. Verify your connection is active within the MCP tab

Cursor MCP docs here for reference

VS Code

1. Open the Settings menu (Command + Comma) and select the correct tab atop the page for your use case

   • Workspace - configures the server in the context of your workspace
   • User - configures the server in the context of your user
   • Note for WSL users: If you're using VS Code with WSL, you'll need to configure WSL-specific settings. Run the Preferences: Open Remote Settings command from the Command Palette (F1) or select the Remote tab in the Settings editor. Local User settings are reused in WSL but can be overridden with WSL-specific settings. Configuring MCP servers in the local User settings will not work properly in a WSL environment.

2. Select Features → Chat

3. Ensure that "Mcp" is Enabled

4. Click "Edit in settings.json" under "Mcp > Discovery"

5. Add your server configuration (dbt) to the provided settings.json file as one of the servers:

{
    "mcp": {
        "inputs": [],
        "servers": {
          "dbt": {
            "command": "<path-to-mcp-executable>",
            "args": ["run", "<path-to-this-directory>/src/dbt_mcp/main.py"]
          }
        }
    }
}

<path-to-mcp-executable> depends on your OS:

• Linux & Mac: <path-to-this-directory>/.venv/bin/mcp
• PC: <path-to-this-directory>/.venv/Scripts/mcp

6. You can start, stop, and configure your MCP servers by:

• Running the MCP: List Servers command from the Command Palette (Control + Command + P) and selecting the server
• Utlizing the keywords inline within the settings.json file

VS Code MCP docs here for reference

Tools

dbt CLI

• build - Executes models, tests, snapshots, and seeds in dependency order
• compile - Generates executable SQL from models, tests, and analyses without running them
• docs - Generates documentation for the dbt project
• ls (list) - Lists resources in the dbt project, such as models and tests
• parse - Parses and validates the project’s files for syntax correctness
• run - Executes models to materialize them in the database
• test - Runs tests to validate data and model integrity
• show - Runs a query against the data warehouse

Allowing your client to utilize dbt commands through this MCP tooling could modify your data models, sources, and warehouse objects. Proceed only if you trust the client and understand the potential impact.

Semantic Layer

• list_metrics - Retrieves all defined metrics
• get_dimensions - Gets dimensions associated with specified metrics
• get_entities - Gets entities associated with specified metrics
• query_metrics - Queries metrics with optional grouping, ordering, filtering, and limiting

Discovery

• get_mart_models - Gets all mart models
• get_all_models - Gets all models
• get_model_details - Gets details for a specific model
• get_model_parents - Gets parent nodes of a specific model
• get_model_children - Gets children modes of a specific model

Remote

• text_to_sql - Generate SQL from natural language requests
• execute_sql - Execute SQL on dbt Cloud's backend infrastructure with support for Semantic Layer SQL syntax.

Contributing

Read CONTRIBUTING.md for instructions on how to get involved!