Use the Dataform remote MCP server

This document shows you how to use the Dataform remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. You can use the Dataform remote MCP server to perform tasks such as managing code assets like notebooks and saved queries, scheduling and executing pipelines within BigQuery, and troubleshooting execution failures The Dataform remote MCP server is enabled when you enable the Dataform API.

Model Context Protocol (MCP) standardizes how large language models (LLMs) and AI applications or agents connect to external data sources. MCP servers let you use their tools, resources, and prompts to take actions and get updated data from their backend service.

What's the difference between local and remote MCP servers?

Local MCP servers

Typically run on your local machine and use the standard input and output streams (stdio) for communication between services on the same device.

Remote MCP servers

Run on the service's infrastructure and offer an HTTP endpoint to AI applications for communication between the AI MCP client and the MCP server. For more information about MCP architecture, see MCP architecture.

Google and Google Cloud remote MCP servers

Google and Google Cloud remote MCP servers have the following features and benefits:

• Simplified, centralized discovery
• Managed global or regional HTTP endpoints
• Fine-grained authorization
• Optional prompt and response security with Model Armor protection
• Centralized audit logging

For information about other MCP servers and information about security and governance controls available for Google Cloud MCP servers, see Google Cloud MCP servers overview.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

If you're using an existing project for this guide, verify that you have the permissions required to complete this guide. If you created a new project, then you already have the required permissions.

Verify that billing is enabled for your Google Cloud project.

Enable the BigQuery and Dataform APIs.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the APIs

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

If you're using an existing project for this guide, verify that you have the permissions required to complete this guide. If you created a new project, then you already have the required permissions.

Verify that billing is enabled for your Google Cloud project.

Enable the BigQuery and Dataform APIs.

Roles required to enable APIs

To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

Enable the APIs

1. Connect your Dataform repository to a Git provider such as GitHub or GitLab. For more information, see Connect to a third-party Git repository.
2. Configure your AI agent with a valid OAuth token using the BigQuery scope.

Required roles

To get the permissions that you need to use the Dataform MCP server, ask your administrator to grant you the following IAM roles on your Google Cloud project:

• Make MCP tool calls: MCP Tool User (roles/mcp.toolUser)
• Create Dataform repositories, workspaces, and edit files: Dataform Editor (roles/dataform.editor)
• Run BigQuery jobs: BigQuery Job User (roles/bigquery.jobUser)

For more information about granting roles, see Manage access to projects, folders, and organizations.

These predefined roles contain the permissions required to use the Dataform MCP server. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to use the Dataform MCP server:

• Make MCP tool calls: mcp.tools.call
• Create Dataform repositories, workspaces, and edit files:
  • dataform.workspaces.create
  • dataform.workspaces.get
  • dataform.workspaces.list
  • dataform.workspaces.delete
  • dataform.workspaces.readFile
  • dataform.workspaces.writeFile
  • dataform.workspaces.moveFile
  • dataform.workspaces.removeFile
  • dataform.workspaces.queryDirectoryContents
  • dataform.workspaces.searchFiles
  • dataform.workspaces.commit
  • dataform.workspaces.push
  • dataform.workspaces.pull
  • dataform.workspaces.fetchHistory
  • dataform.workspaces.fetchDiff
  • dataform.workspaces.fetchFileGitStatuses
  • dataform.compilationResults.create
  • dataform.compilationResults.get