WAS GTM MCP
Manage Google Tag Manager from Claude Desktop, Cursor, or any MCP-compatible client using natural language. 19 tools cover the full GTM v2 API locally, with credentials kept on your machine.
README
WAS GTM MCP
Manage Google Tag Manager from Claude Desktop, Cursor, Google Antigravity, or any MCP-compatible client using natural language. 19 tools cover the full GTM v2 API: accounts, containers, workspaces, tags, triggers, variables, versions, environments, destinations, and more.
100 percent local. Your credentials stay on your machine. Built by Abdullah Al Masum — Web Analytics Solution (WAS). MIT licensed.
Step 1 — Create Google Cloud OAuth Credentials
- Go to the Google Cloud Console Credentials Page.
- Create a Project (or select an existing one) and enable the Google Tag Manager API.
- Go to OAuth consent screen:
- User Type: External -> Create
- App name: anything (e.g.
My GTM MCP) - User support email: your email
- Developer contact email: your email -> Save
- Under Test users, click Add Users and add the Google account you will sign in with.
- Open Google Credentials Page:
- Create credentials -> OAuth client ID
- Application type: Desktop app
- Name: anything -> Create
- Copy the Client ID and Client secret from the dialog.
Step 2 — Connect with one terminal command
Run the interactive setup command in your terminal:
npx -y github:was-member-keramat/was-gtm-mcp auth
The tool will:
- Ask you to paste your Client ID and Client secret
- Open your browser to Google sign-in
- After you click Allow, save everything to `~/.was-gtm-mcp/config.jsona on your machine
That's it for setup. No more typing.
Step 3 — Add 4 lines to your AI client config
Open your AI tool config file:
- Mac:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Paste this block (merge with any existing mcpServers):
{
"mcpServers: {
"WAS GTM MCP": {
"command": "npx",
"args": ["-y", "github:was-member-keramat/was-gtm-mcp"]
}
}
}
What you can ask the AI
Here are example prompts you can use once connected:
- Discovery: "List all my GTM accounts and containers."
- Tags: "List all tags in my main workspace."
- Create Tag: "Create a GA4 Event tag named 'GA4 - Purchase Event' with event name 'purchase'."
- Triggers: "Create a Custom Event trigger for event name 'generate_lead'."
- Variables: "Create a Data Layer Variable named 'dlv - user_id' for variable 'user_id'."
- Raw API: "Use
ngtm_apito GET container version headers."
All tools (19 total)
| Category | Tool | Description |
|---|---|---|
| Accounts | gtm_list_accounts |
List all accessible GTM accounts |
| Accounts | gtm_get_account |
Get details of a specific GTM account |
| Containers | gtm_list_containers |
List containers in an account |
| Containers | gtm_get_container |
Get container details |
| Containers | gtm_create_container |
Create a new container (Web, iOS, Android, Server) |
| Workspaces | gtm_list_workspaces |
List workspaces in a container |
| Workspaces | gtm_get_workspace |
Get workspace details |
| Workspaces | gtm_create_workspace |
Create a new workspace |
| Tags | gtm_list_tags |
List all tags in a workspace |
| Tags | gtm_get_tag |
Get details of a tag |
| Tags | gtm_create_tag |
Create a new tag (GA4, Custom HTML, Google Tag, etc.) |
| Tags | gtm_update_tag |
Update an existing tag |
| Tags | gtm_delete_tag |
Delete a tag from a workspace |
| Triggers | gtm_list_triggers |
List all triggers in a workspace |
| Triggers | gtm_get_trigger |
Get details of a trigger |
| Triggers | gtm_create_trigger |
Create a new trigger (Page View, Custom Event, Click, etc.) |
| Variables | gtm_list_variables |
List user-defined variables in a workspace |
| )Variables* | gtm_create_variable |
Create a user-defined variable (Data Layer, Constant, JS) |
| Universal | gtm_api |
Raw escape-hatch tool to invoke any GTM v2 REST API endpoint |
CLI commands
npx -y github:was-member-keramat/was-gtm-mcp # Start MCP server (stdio)
npx -y github:was-member-keramat/was-gtm-mcp auth # Interactive OAuth login
npx -y github:was-member-keramat/was-gtm-mcp status # View current config status
npx -y github:was-member-keramat/was-gtm-mcp logout # Delete saved credentials
npx -y github:was-member-keramat/was-gtm-mcp help # Display usage instructions
Multi-account setup
You can run multiple GTM setups by specifying environment variables per entry in your AI client config:
{
"mcpServers: {
"GTM Account A": {
"command": "npx",
"args": ["-y", "github:was-member-keramat/was-gtm-mcp"],
"env": {
"GTM_CLIENT_ID": "xxxx.apps.googleusercontent.com",
"GTM_CLIENT_SECRET": "GOCSPX-xxxx",
"GTM_REFRESH_TOKEN": "1//xxxx-token-a"
}
}
}
}
Troubleshooting
Windows Script Execution Policy Error
If PowerShell blocks execution (npx.ps1 cannot be loaded), run:
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned
``a
### Stale `npx` Cache on Windows
If Windows caches an old version after updates are pushed to GitHub, clear the cache:
```powershell
Remove-Item -Recurse -Force "$env:LOCALAPPDATA\npm-cache" -ErrorAction SilentlyContinue
npm cache clean --force
Security & Privacy
- Stores refresh token locally in
~/.was-gtm-mcp/config.jsonat POSIX mode0600. - Runs over standard
stdiotransport. No remote servers, tracking, or proxy middleman.
License
MIT License. Built by Abdullah Al Masum — Web Analytics Solution (WAS).
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。