by ahonn
Analyze your site's SEO metrics and search performance data with Google Search Console webmaster tools for improved visi
Analyze SEO metrics and search performance data.
Google Search Console is a community-built MCP server published by ahonn that provides AI assistants with tools and capabilities via the Model Context Protocol. Analyze your site's SEO metrics and search performance data with Google Search Console webmaster tools for improved visi It is categorized under analytics data.
You can install Google Search Console in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Google Search Console is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated Google Search Console against two servers with overlapping tools; this profile had the clearer scope statement.
Google Search Console has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, Google Search Console benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired Google Search Console into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: Google Search Console is the kind of server we cite when onboarding engineers to host + tool permissions.
Google Search Console is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: Google Search Console surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Google Search Console is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Google Search Console reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired Google Search Console into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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A Model Context Protocol (MCP) server providing comprehensive access to Google Search Console data with enhanced analytics capabilities.
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.
npm install mcp-server-gsc
To obtain Google Search Console API credentials:
{
"mcpServers": {
"gsc": {
"command": "npx",
"args": ["-y", "mcp-server-gsc"],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "/path/to/credentials.json"
}
}
}
}
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
}
}
MIT
Contributions are welcome! Please read our contributing guidelines before submitting pull requests.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.