# Google Search Console MCP (GSC) (`smacient/gsc-mcp-worker`) Actor A comprehensive Google Search Console Model Context Protocol (MCP) Actor that provides deep SEO analytics, keyword cannibalization detection, and search performance insights using your GSC data. Ideal for businesses, agencies, and SEO professionals seeking advanced search analytics. - **URL**: https://apify.com/smacient/gsc-mcp-worker.md - **Developed by:** [Tacheon Digital](https://apify.com/smacient) (community) - **Categories:** SEO tools, MCP servers, Integrations - **Stats:** 12 total users, 1 monthly users, 100.0% runs succeeded, 3 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## GSC MCP Worker - Complete Usage Guide ### Overview A powerful Google Search Console Model Context Protocol (MCP) Worker Actor that delivers comprehensive SEO insights and search performance analysis using your Google Search Console data. Perfect for SEO professionals, marketers, and agencies to unlock advanced keyword cannibalization detection, unbranded traffic analysis, and long-tail opportunities with flexible pay-per-event pricing. ### Features - **Deep SEO Analytics:** Advanced search performance insights beyond GSC interface limitations - **Keyword Cannibalization Detection:** Identify internal competition where multiple pages compete for same queries - **Unbranded Traffic Analysis:** Filter branded searches to show pure organic performance - **Long-Tail Opportunities:** Discover high-impression, low-click keywords for optimization - **Period Comparisons:** Track performance changes across different time periods - **Bulk Data Access:** Process up to 25,000 rows per query for comprehensive analysis - **MCP Integration:** Seamless Claude AI workflows with transparent pay-per-event pricing ### Getting Started #### Step 1: Connect to Claude AI - Open Claude AI at https://claude.ai - Navigate to the integrations or MCP settings #### Step 2: Add MCP Connection Use the following MCP Connector URL: `https://apify-gscmcp.smacient.com/mcp` Then you will be redirected to authenticate your Google account associated with your Search Console. #### Step 3: Authenticate with Google - Sign in using the **Google account associated with your Search Console properties** - This ensures secure and authorized access to your search performance data - Make sure you have appropriate permissions for the GSC properties you want to access #### Step 4: Get Your Apify API Token - Visit your Apify Console - Navigate to Settings → Integrations - Copy your **Apify API token** - Paste this token when prompted during MCP setup #### Step 5: Start Using GSC MCP Worker You're now ready to use the GSC MCP Worker! Ask Claude to: - Analyze search performance data - Detect keyword cannibalization - Find unbranded traffic insights - Discover long-tail keyword opportunities - Compare performance between periods - List your verified properties ### Available Tools #### Core Analytics Tools | Tool | Description | Use Case | |------|-------------|----------| | querySearchAnalytics | Custom search performance reports | Get clicks, impressions, CTR, position by query/page/device | | getTopPerformingPages | Best-performing pages analysis | Identify your highest-traffic pages | | comparePerformancePeriods | Period-over-period comparison | Month-over-month, year-over-year SEO analysis | | getUnbrandedTraffic | Non-branded keyword analysis | Measure pure organic search performance | #### Advanced SEO Tools | Tool | Description | Use Case | |------|-------------|----------| | detectQueryCannibalization | Keyword competition analysis | Find pages competing for same keywords | | findLongTailOpportunities | Low-hanging fruit discovery | High-impression, low-click optimization targets | #### Management Tools | Tool | Description | Use Case | |------|-------------|----------| | listVerifiedSites | List all verified properties | Find your GSC property URLs | | getUserInfo | Account overview | Quick summary of accessible sites | ### Example Queries #### Basic Search Performance "Show me the top 20 performing pages for my website over the last 30 days" #### Keyword Cannibalization "Detect any keyword cannibalization issues for my site in the last 3 months" #### Unbranded Analysis "Analyze my unbranded organic traffic, excluding my company name and brand terms" #### Opportunity Discovery "Find long-tail keyword opportunities with high impressions but low clicks" #### Period Comparison "Compare my search performance from last month vs the month before" #### Optimization Tips - Start with `getTopPerformingPages` to understand your best content - Use `detectQueryCannibalization` to identify optimization priorities - Leverage `findLongTailOpportunities` for quick SEO wins - Monitor trends with `comparePerformancePeriods` #### Prerequisites - Google Search Console account with verified properties - Appropriate permissions for sites you want to analyze - Apify account with API token - Claude AI with MCP integration enabled --- **Ready to unlock your SEO potential?** Connect the GSC MCP Worker to Claude AI and start getting actionable search insights in minutes! # Actor input Schema ## `googleTokens` (type: `object`): Google OAuth tokens obtained from MCP server ## `toolName` (type: `string`): Name of the GSC tool to execute ## `toolArgs` (type: `object`): Arguments for the selected tool ## Actor input object example ```json { "toolName": "listVerifiedSites", "toolArgs": {} } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("smacient/gsc-mcp-worker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("smacient/gsc-mcp-worker").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call smacient/gsc-mcp-worker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=smacient/gsc-mcp-worker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Search Console MCP (GSC)", "description": "A comprehensive Google Search Console Model Context Protocol (MCP) Actor that provides deep SEO analytics, keyword cannibalization detection, and search performance insights using your GSC data. Ideal for businesses, agencies, and SEO professionals seeking advanced search analytics.", "version": "1.0", "x-build-id": "VBLRFH7hhntf2YKZk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/smacient~gsc-mcp-worker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-smacient-gsc-mcp-worker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/smacient~gsc-mcp-worker/runs": { "post": { "operationId": "runs-sync-smacient-gsc-mcp-worker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/smacient~gsc-mcp-worker/run-sync": { "post": { "operationId": "run-sync-smacient-gsc-mcp-worker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "googleTokens", "toolName" ], "properties": { "googleTokens": { "title": "Google OAuth Tokens", "required": [ "access_token" ], "type": "object", "description": "Google OAuth tokens obtained from MCP server", "properties": { "access_token": { "title": "Google Access Token", "type": "string", "description": "Google access token", "editor": "textfield" }, "refresh_token": { "title": "Google Refresh Token", "type": "string", "description": "Google refresh token", "editor": "textfield" } } }, "toolName": { "title": "Tool Name", "enum": [ "listVerifiedSites", "querySearchAnalytics", "getTopPerformingPages", "comparePerformancePeriods", "detectQueryCannibalization", "getUnbrandedTraffic", "findLongTailOpportunities", "getUserInfo" ], "type": "string", "description": "Name of the GSC tool to execute", "default": "listVerifiedSites" }, "toolArgs": { "title": "Tool Arguments", "type": "object", "description": "Arguments for the selected tool", "properties": { "siteUrl": { "title": "Site URL", "type": "string", "description": "Your site URL or domain" }, "startDate": { "title": "Start Date", "type": "string", "description": "Start date (YYYY-MM-DD)" }, "endDate": { "title": "End Date", "type": "string", "description": "End date (YYYY-MM-DD)" }, "queryDimensions": { "title": "Query Dimensions", "type": "array", "description": "Dimensions like query, page, country, device, date", "items": { "type": "string" } }, "brandKeywords": { "title": "Brand Keywords", "type": "array", "description": "List of brand terms to exclude", "items": { "type": "string" } }, "rankingMetric": { "title": "Ranking Metric", "type": "string", "description": "clicks, impressions, ctr, or position" } }, "default": {} } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```