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Google Search Console MCP Server

Google Search Console MCP Server

Local
github.com/ahonnSearch281Updated 8w ago

About Google Search Console MCP Server

Google Search Console MCP Server is an MCP server that gives access to Google Search Console performance data with enhanced analytics. It is aimed at SEO practitioners and site owners who want to query search data through an AI assistant.

Key features

  1. 01Search analytics rows up to 25,000 per request
  2. 02Regex and multiple filter operators
  3. 03Analysis by query, page, country, device and search appearance
  4. 04Automatic quick wins detection
  5. 05Customizable date ranges

Use cases

  1. 01Reviewing search performance over a chosen period
  2. 02Filtering query data by device or country
  3. 03Finding pages with good positions but low click-through rates

MCP support

The server exposes a search_analytics MCP tool that lets MCP clients query Search Console performance data.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"gsc": {
"command": "npx",
"args": [
"-y",
"mcp-server-gsc"
],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "<GOOGLE_APPLICATION_CREDENTIALS>"
}
}
}
}
Run commandnpx -y mcp-server-gsc
Environment variables to set
GOOGLE_APPLICATION_CREDENTIALSSecret

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmmcp-server-gsc

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Google Search Console MCP Server

A Model Context Protocol (MCP) server providing comprehensive access to Google Search Console data with enhanced analytics capabilities.

Features

  • Enhanced Search Analytics: Retrieve up to 25,000 rows of performance data
  • Advanced Filtering: Support for regex patterns and multiple filter operators
  • Quick Wins Detection: Automatically identify optimization opportunities
  • Rich Dimensions: Query, page, country, device, and search appearance analysis
  • Flexible Date Ranges: Customizable reporting periods with historical data access
Sponsored by
macuse.app

macuse.app is a native macOS application that gives your AI superpowers by integrating AI assistants with macOS apps like Calendar, Mail, and Notes, plus universal UI control for any application. Supports Claude Desktop, Cursor, and Raycast with one-click setup. Privacy-first, runs locally.

Prerequisites

  • Node.js 18 or later
  • Google Cloud Project with Search Console API enabled
  • Service Account credentials with Search Console access

Installation

npm install mcp-server-gsc

Authentication Setup

To obtain Google Search Console API credentials:

  1. Visit the Google Cloud Console
  2. Create a new project or select an existing one
  3. Enable the API:
  1. Create credentials:
  • Navigate to "APIs & Services" > "Credentials"
  • Click "Create Credentials" > "Service Account"
  • Fill in the service account details
  • Create a new key in JSON format
  • The credentials file (.json) will download automatically
  1. Grant access:
  • Open Search Console
  • Add the service account email (format: [email protected]) as a property administrator

Usage

Claude Desktop Configuration
{
  "mcpServers": {
    "gsc": {
      "command": "npx",
      "args": ["-y", "mcp-server-gsc"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/credentials.json"
      }
    }
  }
}

Available Tools

search_analytics

Get comprehensive search performance data from Google Search Console with enhanced analytics capabilities.

Required Parameters:

  • siteUrl: Site URL (format: http://www.example.com/ or sc-domain:example.com)
  • startDate: Start date (YYYY-MM-DD)
  • endDate: End date (YYYY-MM-DD)

Optional Parameters:

  • dimensions: Comma-separated list (query, page, country, device, searchAppearance, date)
  • type: Search type (web, image, video, news, discover, googleNews)
  • aggregationType: Aggregation method (auto, byNewsShowcasePanel, byProperty, byPage)
  • rowLimit: Maximum rows to return (default: 1000, max: 25000)
  • dataState: Data freshness (all or final, default: final)

Filter Parameters:

  • pageFilter: Filter by page URL (supports regex with regex: prefix)
  • queryFilter: Filter by search query (supports regex with regex: prefix)
  • countryFilter: Filter by country ISO 3166-1 alpha-3 code (e.g., USA, CHN)
  • deviceFilter: Filter by device type (DESKTOP, MOBILE, TABLET)
  • searchAppearanceFilter: Filter by search feature (e.g., AMP_BLUE_LINK, AMP_TOP_STORIES)
  • filterOperator: Operator for filters (equals, contains, notEquals, notContains, includingRegex, excludingRegex)

Quick Wins Detection:

  • detectQuickWins: Enable automatic detection of optimization opportunities (default: false)
  • quickWinsConfig: Configuration for quick wins detection:
    • positionRange: Position range to consider (default: [4, 20])
    • minImpressions: Minimum impressions threshold (default: 100)
    • minCtr: Minimum CTR percentage (default: 1)

Example - Basic Query:

{
  "siteUrl": "https://example.com",
  "startDate": "2024-01-01",
  "endDate": "2024-01-31",
  "dimensions": "query,page",
  "rowLimit": 5000
}

Example - Advanced Filtering with Regex:

{
  "siteUrl": "https://example.com",
  "startDate": "2024-01-01",
  "endDate": "2024-01-31",
  "dimensions": "page,query",
  "queryFilter": "regex:(AI|machine learning|ML)",
  "filterOperator": "includingRegex",
  "deviceFilter": "MOBILE",
  "rowLimit": 10000
}

Example - Quick Wins Detection:

{
  "siteUrl": "https://example.com",
  "startDate": "2024-01-01",
  "endDate": "2024-01-31",
  "dimensions": "query,page",
  "detectQuickWins": true,
  "quickWinsConfig": {
    "positionRange": [4, 15],
    "minImpressions": 500,
    "minCtr": 2
  }
}

License

MIT

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting pull requests.

Comments

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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.