configuring-dbt-mcp-server

★ 605

by dbt-labs · part of dbt-labs/dbt-agent-skills

configuring-dbt-mcp-server — an installable skill for AI agents, published by dbt-labs/dbt-agent-skills.

🔥🔥🔥FreeQuick setup

#git

🧩 One of 7 skills in the dbt-labs/dbt-agent-skills package — works on its own, and pairs well with its siblings.

configuring-dbt-mcp-server — an installable skill for AI agents, published by dbt-labs/dbt-agent-skills.

Inspect the full instructions your agent will receiveExpand

This is the exact playbook injected into your agent when the skill activates — shown here so you can audit it before installing. You don't need to read it to use the skill.

name: configuring-dbt-mcp-server description: Generates MCP server configuration JSON, resolves authentication setup, and validates server connectivity for dbt. Use when setting up, configuring, or troubleshooting the dbt MCP server for AI tools like Claude Desktop, Claude Code, Cursor, or VS Code. user-invocable: false metadata: author: dbt-labs

Configure dbt MCP Server

Overview

The dbt MCP server connects AI tools to dbt's CLI, Semantic Layer, Discovery API, and Admin API. This skill guides users through setup with the correct configuration for their use case.

Decision Flow

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flowchart TB
    start([User wants dbt MCP]) --> q1{Local or Remote?}
    q1 -->|dev workflows,<br>CLI access needed| local[Local Server<br>uvx dbt-mcp]
    q1 -->|consumption only,<br>no local install| remote[Remote Server<br>HTTP endpoint]
    local --> q2{Which client?}
    remote --> q2
    q2 --> claude_desktop[Claude Desktop]
    q2 --> claude_code[Claude Code]
    q2 --> cursor[Cursor]
    q2 --> vscode[VS Code]
    claude_desktop --> config[Generate config<br>+ test setup]
    claude_code --> config
    cursor --> config
    vscode --> config

Questions to Ask

1. Server Type

Ask: "Do you want to use the local or remote dbt MCP server?"

Local Server	Remote Server
Runs on your machine via `uvx`	Connects via HTTP to dbt platform
Required for development (authoring models, tests, docs) but can also connect to the dbt platform for consumption (querying metrics, exploring metadata)	Best for consumption (querying metrics, exploring metadata)
Supports dbt CLI commands (run, build, test, show)	No CLI commands (run, build, test)
Works without a dbt platform account but can also connect to the dbt platform for development (authoring models, tests, docs)	Requires dbt platform account
No credit consumption	Consumes dbt Copilot credits

2. MCP Client

Ask: "Which MCP client are you using?"

Claude Desktop
Claude Code (CLI)
Cursor
VS Code

3. Use Case (Local Server Only)

Ask: "What's your use case?"

CLI Only	Platform Only	Platform + CLI
dbt Core/Fusion users	dbt Cloud without local project	Full access to both
No platform account needed	OAuth or token auth	Requires paths + credentials

4. Tools to Enable

Ask: "Which tools do you want enabled?" (show defaults)

Tool Category	Default	Environment Variable
dbt CLI (run, build, test, compile)	Enabled	`DISABLE_DBT_CLI=true` to disable
Semantic Layer (metrics, dimensions)	Enabled	`DISABLE_SEMANTIC_LAYER=true` to disable
Discovery API (models, lineage)	Enabled	`DISABLE_DISCOVERY=true` to disable
Admin API (jobs, runs)	Enabled	`DISABLE_ADMIN_API=true` to disable
SQL (text_to_sql, execute_sql)	Disabled	`DISABLE_SQL=false` to enable
Codegen (generate models/sources)	Disabled	`DISABLE_DBT_CODEGEN=false` to enable

Credential Security

Always use environment variable references (e.g., ${DBT_TOKEN}) instead of literal token values in configuration files that may be committed to version control
Never log, display, or echo token values in terminal output
When using .env files, ensure they are added to .gitignore to prevent accidental commits
Recommend users rotate tokens regularly and use the minimum required permission set

Verification Steps

Test Local Server Config

Recommended: Use .env file

Create a .env file in your project root directory and add minimum environment variables for the CLI tools:

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DBT_PROJECT_DIR=/path/to/project
DBT_PATH=/path/to/dbt

Test the server:

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uvx --env-file .env dbt-mcp

Alternative: Environment variables

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# Temporary test (variables only last for this session)
export DBT_PROJECT_DIR=/path/to/project
export DBT_PATH=/path/to/dbt
uvx dbt-mcp

No errors = successful configuration.

Verify in Client

After setup, ask the AI:

"What dbt tools do you have access to?"
"List my dbt metrics" (if Semantic Layer enabled)
"Show my dbt models" (if Discovery enabled)

See Troubleshooting for common issues and fixes.

See Environment Variable Reference for the full list of supported variables.

Copy & paste — that's it

npx skills add https://github.com/dbt-labs/dbt-agent-skills --skill configuring-dbt-mcp-server

Run this in your project — your agent picks the skill up automatically.

Prerequisites

Local Server

Install uv: https://docs.astral.sh/uv/getting-started/installation/
Have a dbt project (for CLI commands)
Find paths:
- DBT_PROJECT_DIR: Folder containing dbt_project.yml
  - macOS/Linux: pwd from project folder
  - Windows: Full path with forward slashes (e.g., C:/Users/name/project)
- DBT_PATH: Path to dbt executable
  - macOS/Linux: which dbt
  - Windows: where dbt

Remote Server

dbt Cloud account with AI features enabled
Production environment ID (from Orchestration page)
Personal access token or service token

See How to Find Your Credentials for detailed guidance on obtaining tokens and IDs.

Configuration Templates

Local Server - CLI Only

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{
  "mcpServers": {
    "dbt": {
      "command": "uvx",
      "args": ["dbt-mcp"],
      "env": {
        "DBT_PROJECT_DIR": "/path/to/your/dbt/project",
        "DBT_PATH": "/path/to/dbt"
      }
    }
  }
}

Local Server - Platform + CLI (OAuth)

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{
  "mcpServers": {
    "dbt": {
      "command": "uvx",
      "args": ["dbt-mcp"],
      "env": {
        "DBT_HOST": "https://your-subdomain.us1.dbt.com",
        "DBT_PROJECT_DIR": "/path/to/project",
        "DBT_PATH": "/path/to/dbt"
      }
    }
  }
}

Local Server - Platform + CLI (Token Auth)

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{
  "mcpServers": {
    "dbt": {
      "command": "uvx",
      "args": ["dbt-mcp"],
      "env": {
        "DBT_HOST": "cloud.getdbt.com",
        "DBT_TOKEN": "${DBT_TOKEN}",
        "DBT_ACCOUNT_ID": "${DBT_ACCOUNT_ID}",
        "DBT_PROD_ENV_ID": "${DBT_PROD_ENV_ID}",
        "DBT_PROJECT_DIR": "/path/to/project",
        "DBT_PATH": "/path/to/dbt"
      }
    }
  }
}

Local Server - Using .env File

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

.env file contents:

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DBT_HOST=cloud.getdbt.com
DBT_TOKEN=<set-via-env-or-secret-manager>
DBT_ACCOUNT_ID=<your-account-id>
DBT_PROD_ENV_ID=<your-prod-env-id>
DBT_DEV_ENV_ID=<your-dev-env-id>
DBT_USER_ID=<your-user-id>
DBT_PROJECT_DIR=/path/to/project
DBT_PATH=/path/to/dbt

Remote Server

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{
  "mcpServers": {
    "dbt": {
      "url": "https://cloud.getdbt.com/api/ai/v1/mcp/",
      "headers": {
        "Authorization": "Token ${DBT_TOKEN}",
        "x-dbt-prod-environment-id": "${DBT_PROD_ENV_ID}"
      }
    }
  }
}

Additional headers for SQL/Fusion tools:

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{
  "headers": {
    "Authorization": "Token ${DBT_TOKEN}",
    "x-dbt-prod-environment-id": "${DBT_PROD_ENV_ID}",
    "x-dbt-dev-environment-id": "${DBT_DEV_ENV_ID}",
    "x-dbt-user-id": "${DBT_USER_ID}"
  }
}

Client-Specific Setup

Claude Desktop

Click Claude menu in system menu bar (not in-app)
Select Settings...
Go to Developer tab
Click Edit Config
Add the JSON configuration
Save and restart Claude Desktop
Verify: Look for MCP server indicator in bottom-right of input box

Config location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Claude Code (CLI)

Run:

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claude mcp add dbt -s user -- uvx dbt-mcp

This adds the server to your user scope/config (on this system: ~/.claude.json).

For a project-specific setup, run:

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claude mcp add dbt -s project -- uvx dbt-mcp

This adds the server to .mcp.json in your project root.

Alternatively, you can use the manual configuration below.

Manual configuration: Edit ~/.claude.json (user scope) or create .mcp.json (project scope) in your project root:

~/.claude.json: Global across all projects
.mcp.json: Project-specific, can be committed to version control for team sharing. If using token auth, use environment variable references — never commit literal tokens.

For project-specific dbt setups, use .mcp.json so your team shares the same configuration.

Once the config is created, make sure to add the JSON configuration under the mcpServers key.

Cursor

Open Cursor menu → Settings → Cursor Settings → MCP
Add the JSON configuration
Update paths and credentials
Save

VS Code

Open Command Palette (Cmd/Ctrl + Shift + P)
Run "MCP: Open User Configuration" (or Workspace for project-specific)
Add the JSON configuration (note: VS Code uses servers not mcpServers):

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{
  "servers": {
    "dbt": {
      "command": "uvx",
      "args": ["dbt-mcp"],
      "env": {
        "DBT_PROJECT_DIR": "/path/to/project",
        "DBT_PATH": "/path/to/dbt"
      }
    }
  }
}

Open Settings → Features → Chat → Enable MCP
Verify: Run "MCP: List Servers" from Command Palette

WSL Users: Configure in Remote settings, not local user settings:

Run "Preferences: Open Remote Settings" from Command Palette
Use full Linux paths (e.g., /home/user/project, not Windows paths)