GTM MCP Server

Let AI manage your Google Tag Manager containers.

Create tags, audit configurations, generate tracking plans, and publish changes, all through natural conversation with Claude, ChatGPT, Gemini, Cursor, and more.

URL: https://mcp.gtmeditor.com

Table of Contents

• Supported AI Clients
• What Can You Do?
• Quick Start
  • Claude (Web & Desktop)
  • ChatGPT
  • Gemini CLI
  • Cursor
• Features
  • Tag Management
  • Trigger Management
  • Container Operations
  • Server-Side Containers
  • Community Template Gallery
  • AI-Powered Workflows
• Use Cases
  • Build Complete Tracking Setups
  • Implement Consent Management
  • Bulk Operations & Renaming
  • Custom Variables & Logic
  • For Agencies
• How It Works
• Safety Features
• Self-Hosting
  • Service Account Mode (S2S)
  • Docker Setup
  • Google Cloud Setup
• Available Tools
  • Read Operations
  • Utility
  • Write Operations
  • Server-Side Container Tools
  • Publishing
  • Templates
• Resources & Prompts
  • Resources (URI-based access)
  • Prompts (Workflow templates)
• Better AI Context
  • llms.txt — For Any LLM or Agent
  • Claude Code Skill — Guided Workflows
  • GTM API Skill — API Reference
• Architecture
• Known Issues
• Links
• Author
• License

Supported AI Clients

The server is client-agnostic — any MCP client that supports OAuth 2.1 with PKCE over HTTP transport should work out of the box, including clients that use Dynamic Client Registration (RFC 7591) and those that don't.

What Can You Do?

Ask your AI assistant to:

• "List all my GTM containers"
• "Create a GA4 event tag for form submissions"
• "Audit this container for issues and duplicates"
• "Generate a tracking plan document for the marketing team"
• "Set up ecommerce tracking for purchases"
• "Publish the changes we just made"

No more clicking through the GTM interface. No more copy-pasting configurations. Just describe what you need.

Quick Start

Claude (Web & Desktop)

Claude.ai:

1. Go to Settings → Connectors → Add Custom Connector
2. Enter: https://mcp.gtmeditor.com
3. Click Add and sign in with Google

Claude Code (CLI):

claude mcp add -t http gtm https://mcp.gtmeditor.com

ChatGPT

1. Go to OpenAI Apps Platform
2. Add an MCP integration with URL: https://mcp.gtmeditor.com
3. Authorize with your Google account

Gemini CLI

gemini mcp add --transport http --url https://mcp.gtmeditor.com gtm

Cursor

1. Open Settings > MCP
2. Click Add new MCP server
3. Set type to URL and enter: https://mcp.gtmeditor.com/authorize
4. Authorize with your Google account

Or add to your .cursor/mcp.json:

{
  "mcpServers": {
    "gtm": {
      "url": "https://mcp.gtmeditor.com/authorize"
    }
  }
}

Features

Tag Management

Create and modify any GTM tag type:

• GA4 Configuration & Events — Set up Google Analytics 4 with proper measurement IDs
• Ecommerce Tracking — Purchase, add-to-cart, view-item events
• Custom HTML — Inject scripts, pixels, and custom code
• Custom Image — Tracking pixels with cache busting

Trigger Management

Build triggers for any scenario:

• Page views (all pages or specific URLs)
• Custom dataLayer events
• Click tracking
• Form submissions
• Timer-based triggers
• Trigger groups for complex conditions

Container Operations

• Browse accounts, containers, and workspaces
• Create versions from workspace changes
• Publish versions to go live
• Organize with folders
• Enable/disable built-in variables

Server-Side Containers

Full support for server-side GTM containers:

• Clients — Create, update, and delete server-side clients (e.g. GA4 client)
• Transformations — Control event parameters with allow, exclude, and augment rules

Community Template Gallery

Import templates from Google's Community Template Gallery:

• "Import the iubenda cookie consent template"
• "Add Cookiebot to my container"
• "Set up Facebook Pixel using the gallery template"

The AI will search for the template, find the GitHub repository, and import it automatically.

AI-Powered Workflows

Container Audit "Audit my container for issues" — Analyzes your workspace for:

• Naming inconsistencies
• Duplicate tags
• Orphaned triggers
• Security concerns
• Best practice violations

Tracking Plan Generation "Generate a tracking plan" — Creates markdown documentation of:

• All events and their triggers
• Data layer requirements
• Variable definitions
• Implementation notes

GA4 Setup Recommendations "Help me set up GA4 for ecommerce" — Recommends:

• Which tags to create
• Trigger configurations
• Required variables
• Data layer implementation code

Use Cases

Build Complete Tracking Setups

Ask AI to create a full GA4 ecommerce implementation from scratch:

• "Set up GA4 ecommerce tracking for my store"
• Creates 12+ tags (configuration + all ecommerce events)
• Creates matching triggers for each dataLayer event
• Creates data layer variables for items, currency, value, transaction_id
• Follows Google's recommended event naming and parameters

Implement Consent Management

Integrate privacy tools like OneTrust with your tracking:

• "Make GA4 fire only when analytics consent is granted"
• Creates consent-checking variables
• Sets up conditional triggers
• Updates existing tags to respect user choices

Bulk Operations & Renaming

Manage containers at scale:

• "Add 'ecom -' prefix to all ecommerce triggers"
• "Update all tags to use a measurement ID variable"
• Rename, update, or organize dozens of items through conversation

Custom Variables & Logic

Create sophisticated tracking logic:

• "Create a variable that returns the local timestamp"
• "Add a custom parameter to the purchase tag"
• Custom JavaScript variables, data layer mappings, and more

For Agencies

• Manage multiple client containers (7+ accounts shown in demo)
• Standardize implementations across clients
• Rapid setup for new projects
• Version and publish changes safely

How It Works

The GTM MCP Server connects AI assistants to the Google Tag Manager API using the Model Context Protocol. When you ask Claude or ChatGPT to manage your GTM, it:

1. Authenticates with your Google account (OAuth 2.1)
2. Reads your container configurations
3. Executes the changes you request
4. Confirms before destructive operations

Your credentials are never stored—the server uses token-based authentication that you can revoke anytime from your Google account.

Safety Features

• Confirmation required for deletions and publishing
• Workspace-only changes — nothing goes live until you publish
• Version control — all changes create a version first
• Audit logging — track what was changed

Self-Hosting

Want to run your own instance?

Service Account Mode (S2S)

Self-hosted deployments can use a Google Service Account so the whole team shares access — no individual GTM permissions needed.

How it works:

• The server authenticates to Google Tag Manager using a Service Account
• Team members connect with a shared API key — no personal GTM access required
• AI clients (Claude Code, ChatGPT, etc.) still do a one-time OAuth login to the server, but all GTM calls run under the Service Account
• Programmatic clients (scripts, CI/CD, APIs) skip OAuth entirely and use the API key directly

Setup:

1. Create a Service Account in Google Cloud Console → IAM & Admin → Service Accounts
2. In Google Tag Manager → Account → Admin → User Management → add the Service Account email as Account Administrator
3. Download the JSON key file
4. Configure the server:

SERVICE_ACCOUNT_API_KEY=$(openssl rand -hex 32)   # share this with your team
GOOGLE_SERVICE_ACCOUNT_KEY_JSON=$(cat key.json)   # paste JSON content
go run main.go

On GCP (Cloud Run, GKE, Compute Engine): omit GOOGLE_SERVICE_ACCOUNT_KEY_JSON — Workload Identity is used automatically.

Connecting Claude Code:

Add the API key as a pre-configured header so Claude Code uses S2S automatically:

{
  "mcpServers": {
    "gtm": {
      "type": "http",
      "url": "http://your-server:8080",
      "headers": {
        "Authorization": "Bearer your-api-key"
      }
    }
  }
}

Programmatic / API access:

Any HTTP client can call the server directly — no browser, no OAuth:

curl -H "Authorization: Bearer your-api-key" \
     -H "Content-Type: application/json" \
     http://your-server:8080/mcp \
     -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

See examples/gtm_agent.py for a complete Python agent that uses Claude to manage GTM programmatically via the API key.

Docker Setup

git clone https://github.com/paolobietolini/gtm-mcp-server.git
cd gtm-mcp-server

# Create .env file
cat > .env << 'EOF'
GOOGLE_CLIENT_ID=your-client-id.apps.googleusercontent.com
GOOGLE_CLIENT_SECRET=your-client-secret
JWT_SECRET=$(openssl rand -base64 32)
BASE_URL=http://localhost:8080
EOF

# Start the server
docker compose up -d

# Add to Claude
claude mcp add -t http gtm http://localhost:8080

Reverse Proxy (TRUST_PROXY)

When running behind a reverse proxy (Caddy, nginx, Cloudflare), set TRUST_PROXY=true so the rate limiter uses the client's real IP from X-Forwarded-For instead of the proxy's address:

TRUST_PROXY=true

The Docker Compose setup sets this automatically since the container runs behind Caddy. When running the binary directly without a proxy, leave it unset or false — otherwise clients can spoof IPs to bypass rate limiting.

Docker-to-Docker

If another container needs to reach the MCP server via an internal Docker network alias, add ALLOWED_HOSTS to your .env:

ALLOWED_HOSTS=gtm-mcp:8080

This enables dynamic URL resolution for trusted internal hostnames while keeping the server secure against host header injection.

Google Cloud Setup

1. Go to Google Cloud Console
2. Enable the Tag Manager API
3. Create OAuth 2.0 credentials (Web application)
4. Add redirect URIs:
   hljs language-ruby

   Copy

   https://claude.ai/api/mcp/auth_callback
   https://claude.com/api/mcp/auth_callback
   https://chatgpt.com/connector_platform_oauth_redirect
   https://your-domain.com/oauth/callback
   

Available Tools

Read Operations

Utility

Write Operations

Server-Side Container Tools

Publishing

Templates

Resources & Prompts

Resources (URI-based access)

Access GTM data via structured URIs:

gtm://accounts
gtm://accounts/{id}/containers
gtm://accounts/{id}/containers/{id}/workspaces
gtm://accounts/.../workspaces/{id}/tags
gtm://accounts/.../workspaces/{id}/triggers
gtm://accounts/.../workspaces/{id}/variables

Prompts (Workflow templates)

Better AI Context

The server provides two resources to help AI assistants use it more effectively — one for any LLM or agent, and one specifically for Claude Code users.

llms.txt — For Any LLM or Agent

The server hosts an llms.txt file at its root that any LLM or agent can fetch for context. It documents the GTM hierarchy, all available tools, common workflows, safety rules, and the GA4 parameter format.

https://mcp.gtmeditor.com/llms.txt

This follows the llms.txt standard. Agent frameworks that support llms.txt will pick this up automatically. You can also fetch it manually or include it as a system prompt for custom integrations.

Claude Code Skill — Guided Workflows

For Claude Code users, install the GTM MCP skill for guided workflows, anti-patterns to avoid, and step-by-step task patterns:

# One-liner install
curl -sL https://github.com/paolobietolini/gtm-mcp-server/archive/main.tar.gz | tar xz && \
  mkdir -p ~/.claude/skills && \
  cp -r gtm-mcp-server-main/skills/gtm-mcp ~/.claude/skills/ && \
  rm -rf gtm-mcp-server-main

Or clone and copy:

git clone https://github.com/paolobietolini/gtm-mcp-server.git
cp -r gtm-mcp-server/skills/gtm-mcp ~/.claude/skills/

The skill teaches Claude how to discover IDs, create tags with the correct parameter format, follow the publish workflow, and avoid common mistakes.

GTM API Skill — API Reference

For deeper API context (parameter schemas, validation rules, request templates for all entity types), install the GTM API skill:

Claude Code:

curl -sL https://github.com/paolobietolini/gtm-api-for-llms/archive/main.tar.gz | tar xz && \
  mkdir -p ~/.claude/skills && \
  cp -r gtm-api-for-llms-main/skills/gtm-api ~/.claude/skills/ && \
  rm -rf gtm-api-for-llms-main

OpenAI Codex:

curl -sL https://github.com/paolobietolini/gtm-api-for-llms/archive/main.tar.gz | tar xz && \
  mkdir -p ~/.codex/skills && \
  cp -r gtm-api-for-llms-main/skills/gtm-api ~/.codex/skills/ && \
  rm -rf gtm-api-for-llms-main

The GTM API for LLMs repository provides LLM-optimized documentation: request templates, validation rules, workflow algorithms, and complete schemas for all GTM entity types including server-side containers.

Architecture

• Protocol: Model Context Protocol (MCP) over HTTP
• Authentication: OAuth 2.1 with PKCE
• Standards: RFC 8414, RFC 7591, RFC 9728

Known Issues

🐛 autoEventFilter silently dropped by Google Tag Manager API

When creating or updating linkClick, click, or formSubmission triggers via the API, the autoEventFilter field (used for "Some Link Clicks"/"Some Form Submissions" conditions) is silently dropped by the Google Tag Manager API. The API returns 200 OK with a new fingerprint but does not persist the autoEventFilter.

This has been confirmed by HTTP-level debugging: the correct JSON is sent in the request body, but Google's response omits the field. The filter and customEventFilter fields work correctly.

Workaround: Configure autoEventFilter conditions manually through the GTM web interface. The MCP server can read triggers that have autoEventFilter set via the UI.

Status: #33

Links

• GitHub Repository
• GTM API Reference
• MCP Specification

Author

Paolo Bietolini

mcp@paolobietolini.com

License

BSD-3-Clause