What is Dbt Mcp

dbt-mcp is a Model Context Protocol (MCP) server designed to facilitate interactions with dbt resources. It provides tools for executing commands, retrieving model information, and querying the dbt Cloud Semantic Layer.

Use cases

Use cases for dbt-mcp include automating dbt command executions, integrating dbt model insights into applications, and querying semantic metrics directly from dbt Cloud.

How to use

To use dbt-mcp, clone the repository from GitHub, install the necessary dependencies (uv and Task), configure the environment variables in a .env file, and run the server to interact with dbt resources.

Key features

Key features of dbt-mcp include the ability to run commands from dbt Core or dbt Cloud CLI, access model information and transformations, and interact with the dbt Cloud Semantic Layer to retrieve metrics and dimensions.

Where to use

dbt-mcp can be used in data engineering and analytics environments where dbt is utilized for data transformation and modeling, particularly in cloud-based data platforms.

dbt MCP Server

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

Architecture

Setup

1. Install uv
2. Copy the .env.example file locally under a file called .env and set it 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

Configuration for Discovery, Semantic Layer, and Remote Tools

Configuration for Remote Tools

Configuration for dbt CLI

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 <>:

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

Claude Desktop

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

You can find the Claude Desktop logs at ~/Library/Logs/Claude for Mac or %APPDATA%\Claude\logs for Windows.

Cursor

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

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:

<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 + P) and selecting the server
• Utlizing the keywords inline within the settings.json file

VS Code MCP docs here for reference

Troubleshooting

• 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".

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!