This MCP (Model Context Protocol) server provides tools to interact with dbt. Read this blog to learn more. Add comments or questions to GitHub Issues or join us in the community Slack in the #tools-dbt-mcp channel.

• 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.

• 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

• 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 models of a specific model

• 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. Note: using a PAT instead of a service token for DBT_TOKEN is required for this tool.

There are two ways to setup dbt MCP, local and remote. Local setup is best for dbt projects that you are developing in a local IDE. Remote setup is better for building custom applications.

1. Install uv
2. Copy the .env.example file locally under a file called .env and set it with the following environment variable configuration:

It is also possible to set any environment variable supported by your dbt executable (see here for the ones supported in dbt Core).

We automatically set DBT_WARN_ERROR_OPTIONS='{"error": ["NoNodesForSelectionCriteria"]}' so that the MCP server knows if no node is selected when running a dbt command. You can overwrite it if needed but we believe that it provides a better experience when calling dbt from the MCP server, making sure that the tool is selecting valid nodes.

After going through the Setup, you can use dbt-mcp with an MCP client.

Add this configuration to the respective client's config file. Be sure to replace the sections within <>:

{
  "mcpServers": {
    "dbt-mcp": {
      "command": "uvx",
      "args": [
        "--env-file",
        "<path-to-.env-file>",
        "dbt-mcp"
      ]
    },
  }
}

<path-to-.env-file> is where you saved the .env file from the Setup step

Run the following command to add the MCP server to Claude Code:

claude mcp add dbt -- uvx --env-file <path-to-.env-file> dbt-mcp

By default the MCP server is installed in the "local" scope, meaning that it will be active for Claude Code sessions in the current directory for the user who installed it.

It is also possible to install the MCP server:

• in the "user" scope, to have it installed for all Claude Code sessions, independently of the directory used
• in the "project" scope, to create a config file that can be version controlled so that all developers of the same project can have the MCP server already installed

To install it in the project scope, run the following and and commit the .mcp.json file. Be sure to use an env var file path that is the same for all users.

claude mcp add dbt -s project -- uvx --env-file <path-to-.env-file> dbt-mcp

More info on scopes here

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

For debugging, you can find the Claude Desktop logs at ~/Library/Logs/Claude for Mac or %APPDATA%\Claude\logs for Windows.

Note the configuration options here and input your selections with this link:

Cursor MCP docs here for reference

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. Open the command palette Control/Command + Shift + P, and select either "MCP: Open Workspace Folder MCP Configuration" or "MCP: Open User Configuration" depending on whether you want to install the MCP server for this workspace or for all workspaces for the user

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

{
	"servers": {
		"dbt": {
			"command": "uvx",
      "args": [
        "--env-file",
        "<path-to-.env-file>",
        "dbt-mcp"
      ]
		}
	}
}

<path-to-.env-file> is where you saved the .env file from the Setup step

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

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

VS Code MCP docs here for reference

The remote setup doesn't require running dbt MCP locally. Instead, an HTTP connection is made to dbt MCP running within dbt Cloud. Right now, only Semantic Layer & Discovery tools are supported. To get started, get the having the following information:

• dbt Cloud host: Use this to form the full URL. For example, replace <host> here: https://<host>/api/ai/v1/mcp/. It may look like: https://cloud.getdbt.com/api/ai/v1/mcp/.
• Production environment ID: This can be found on the Orchestration page of dbt Cloud. Use this to set a x-dbt-prod-environment-id header.
• Service token: To fully utilize Remote MCP, this needs to be configured for the dbt Semantic Layer by following this guide and have Developer permissions. Add this as a Authorization header with a value like: token <token>. Be sure to replace <token> with the value of your token.

Then you can use these values to connect to the remote server with Streamable HTTP MCP transport. Use the example here as a reference in Python. A similar implementation is possible with SDKs for many other languages.

You can also connect from MCP clients which support remote MCP with headers. For instance, you can connect Cursor to the remote server with the following configuration. Be sure to replace <host>, <token>, <prod-id> with your information:

{
  "mcpServers": {
    "dbt": {
      "url": "https://<host>/api/ai/v1/mcp/",
      "headers": {
        "Authorization": "token <token>",
        "x-dbt-prod-environment-id": "<prod-id>",
      }
    }
  }
}

• Some MCP clients may be unable to find uvx from the JSON config. If this happens, try finding the full path to uvx with which uvx on Unix systems and placing this full path in the JSON. For instance: "command": "/the/full/path/to/uvx".

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