- Home
- MCP servers
- Google Search Console
Google Search Console
- typescript
0
GitHub Stars
typescript
Language
6 months ago
First Indexed
3 months ago
Catalog Refreshed
Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
You can securely connect your AI assistants to Google Search Console data using a Model Context Protocol (MCP) server. This setup lets you ask natural language questions about your SEO data and receive actionable insights without navigating dashboards, enabling faster optimization and governance across multiple properties.
How to use
You will connect an MCP client to the Google Search Console MCP Server and start querying your data through natural language prompts. Start by choosing your access method: use the remote MCP endpoint to leverage the hosted server, or run a local Python MCP server for Claude Desktop or development workflows. Once connected, you can ask questions like which keywords are driving traffic, which pages are losing visibility, or how performance changed before and after a specific update. The MCP server translates your questions into API requests to Google Search Console, aggregates the results, and returns clear, actionable insights you can act on.
How to install
Prerequisites: Node.js and npm for Cloudflare Workers, or Python for a local server. Have your Google Cloud OAuth credentials ready if you are using OAuth-based authentication.
Option A: Cloudflare Workers (recommended for ChatGPT and hosted usage) is the primary deployment path. Install and deploy using the following steps.
cd gsc-mcp-cloud
npm install
# Set OAuth credentials
npx wrangler secret put GOOGLE_CLIENT_ID
npx wrangler secret put GOOGLE_CLIENT_SECRET
# Deploy
npm run deploy
# Visit /auth to authenticate with Google
Option B: Local Python server (for Claude Desktop or local testing) uses a Python runtime configured with FastMCP.
# OAuth Authentication (example)
{
"mcpServers": {
"gscServer": {
"command": "/path/to/mcp-gsc/.venv/bin/python",
"args": ["/path/to/mcp-gsc/gsc_server.py"],
"env": {
"GSC_OAUTH_CLIENT_SECRETS_FILE": "/path/to/client_secrets.json"
}
}
}
}
Additional sections
Configuration in practice involves choosing a connection method and ensuring required credentials are available. If you run the hosted Cloudflare Workers option, you will typically work with an endpoint such as the production URL to access the MCP interface, and you will authenticate via Google OAuth. For local development, you run a Python server from your project directory and supply the necessary environment variables or service account credentials as shown. Security considerations include keeping OAuth client secrets and service account keys private, restricting access to your deployment, and rotating credentials regularly.
Operational notes: expect a data latency of a few days in GSC and be aware of API limitations such as data lookback windows and per-query row limits. You can combine multiple properties within a single conversation to compare performance and identify optimization opportunities.
Available tools
search
Search for relevant documents and data from GSC based on a query
fetch
Retrieve complete document content using a document ID
list_properties
List all Search Console properties with permission levels
add_site
Add a new site to Search Console (URL or domain property)
delete_site
Remove a site from Search Console
get_site_details
Get verification status and ownership details
get_search_analytics
Get search data with customizable dimensions (query, page, country, device)
get_performance_overview
Comprehensive performance summary with totals and daily trends
get_advanced_search_analytics
Advanced queries with sorting, filtering, multiple search types (Web, Image, Video, News, Discover)
compare_search_periods
Compare two time periods to identify trends and changes
get_search_by_page_query
Get queries driving traffic to a specific page
inspect_url_enhanced
Check indexing status, rich results, crawling details, canonical URLs
batch_url_inspection
Inspect up to 10 URLs at once
check_indexing_issues
Find not-indexed pages, canonical conflicts, robots blocking, fetch errors
get_sitemaps
List all sitemaps with status and URL counts
submit_sitemap
Submit or resubmit a sitemap for processing
list_sitemaps_enhanced
Detailed sitemap list with submission dates, content types, warnings
get_sitemap_details
Deep dive into a specific sitemap's status and errors
delete_sitemap
Remove a sitemap from processing
find_high_potential_keywords
Find quick-win keyword opportunities (ranking 11-40, high impressions with low CTR)
check_page_experience
Analyze page experience signals (mobile usability, crawl status, indexing state)
get_coverage_report
Comprehensive indexing analysis (coverage, issues, action items)
analyze_backlinks
Page authority and internal linking (top pages, hubs, orphan pages)
spot_content_opportunities
Identify rising or declining content and refresh candidates
analyze_regional_device_performance
Country and device breakdown (mobile vs desktop, geo performance)
analyze_algorithm_impact
Before/after comparison for algorithm updates with recommendations