MCP Search Analytics Server

@dexter480

About MCP Search Analytics Server

MCP server for GA and GSC data analysis

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-search-analytics": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

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Overview

What is MCP Search Analytics Server?

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.

How to use MCP Search Analytics Server?

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.

Key features of MCP Search Analytics Server

Unified access to GA4 and Search Console data
Real‑time analytics queries via MCP interface
Secure credential management through environment variables
Built‑in credential testing script

Use cases of MCP Search Analytics Server

Query website traffic and search performance data through an MCP‑compatible AI assistant
Combine Google Analytics and Search Console metrics in a single request
Automate analytics data retrieval in AI‑powered data analysis pipelines

FAQ from MCP Search Analytics Server

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.

How do I configure the server?

Copy environment.example to .env and set the ANALYTICS_CREDENTIALS_PATH, GSC_SITE_URL, and GA4_PROPERTY_ID variables with your actual values.

What security precautions are recommended?

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.

How can I test that my setup is working?

Run python test_credentials.py to verify your credentials before starting the server.

Where can I find the full list of Python dependencies?

The requirements.txt file in the repository lists all required Python packages.

Frequently asked questions

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.

How do I configure the server?

Copy `environment.example` to `.env` and set the `ANALYTICS_CREDENTIALS_PATH`, `GSC_SITE_URL`, and `GA4_PROPERTY_ID` variables with your actual values.

What security precautions are recommended?

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.

How can I test that my setup is working?

Run `python test_credentials.py` to verify your credentials before starting the server.

Where can I find the full list of Python dependencies?

The `requirements.txt` file in the repository lists all required Python packages.

Comments

Basic information

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