configuring-dbt-mcp-server

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 compatible MCP clients.
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

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 --> global[Global client config]
    q2 --> project[Project config]
    q2 --> desktop[Desktop MCP client]
    q2 --> editor[Editor MCP client]
    global --> config[Generate config<br>+ test setup]
    project --> config
    desktop --> config
    editor --> 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?"

• Global client config
• Project config
• Desktop MCP client
• Editor MCP client

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

Prerequisites

Local Server

1. Install uv: https://docs.astral.sh/uv/getting-started/installation/
2. Have a dbt project (for CLI commands)
3. 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

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

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

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

Configuration Templates

Local Server - CLI Only

{
  "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)

{
  "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)

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

Local Server - Using .env File

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

.env file contents:

DBT_HOST=cloud.getdbt.com
DBT_TOKEN=your-token
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

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

Additional headers for SQL/Fusion tools:

{
  "headers": {
    "Authorization": "Token your-token",
    "x-dbt-prod-environment-id": "your-prod-env-id",
    "x-dbt-dev-environment-id": "your-dev-env-id",
    "x-dbt-user-id": "your-user-id"
  }
}

Client-Specific Setup

Generic MCP Client

1. Open your MCP client's server configuration.
2. Add the JSON configuration.
3. Save and restart the client.
4. Verify: Confirm the dbt MCP server is listed and reachable.

Kilo

Add the mcp block to your kilo.json config file.

Config locations:

• Global: ~/.config/kilo/kilo.json
• Project: ./kilo.json or ./.kilo/kilo.json in your project root

Project-level configuration takes precedence over global settings. For project-specific dbt setups, use .kilo/kilo.json so your team shares the same configuration.

Add the dbt MCP server under the mcp key:

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

VS Code Extension: Open Kilo Settings > Agent Behaviour > MCP Servers, then click "Edit Global MCP" (or "Edit Project MCP" for project-specific config) and add the config above.

Cursor

1. Open Cursor menu → Settings → Cursor Settings → MCP
2. Add the JSON configuration
3. Update paths and credentials
4. Save

VS Code

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

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

4. Open Settings → Features → Chat → Enable MCP
5. 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)

Verification Steps

Test Local Server Config

Recommended: Use .env file

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

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

2. Test the server:

uvx --env-file .env dbt-mcp

Alternative: Environment variables

# 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.