What are the prerequisites?
Python 3.8+, a Google Cloud Project with the Analytics Reporting API and Search Console API enabled, and a Google Service Account with appropriate permissions.
@dexter480
基本信息
MCP server for GA and GSC data analysis
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-search-analytics": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
MCP Search Analytics Server is a Model Context Protocol (MCP) server that provides unified access to Google Analytics 4 and Google Search Console data, enabling real-time analytics queries through the MCP interface. It is designed for developers and data analysts who need to integrate analytics data into AI‑assisted workflows.
Install Python dependencies (Python 3.8+ required), configure environment variables for your Google service account credentials, Search Console site URL, and GA4 property ID in a .env file, then run the server with python unified_analytics_server.py. Test your credentials first with python test_credentials.py.
Python 3.8+, a Google Cloud Project with the Analytics Reporting API and Search Console API enabled, and a Google Service Account with appropriate permissions.
Copy `environment.example` to `.env` and set the `ANALYTICS_CREDENTIALS_PATH`, `GSC_SITE_URL`, and `GA4_PROPERTY_ID` variables with your actual values.
Never commit credential files (`.json`, `.env`) to version control, store credentials securely, rotate service account keys regularly, and follow the principle of least privilege for API access.
Run `python test_credentials.py` to verify your credentials before starting the server.
The `requirements.txt` file in the repository lists all required Python packages.
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