# Google Analytics MCP (GA4) (`smacient/ga4-mcp-worker`) Actor A powerful Google Analytics 4 Model Context Protocol (MCP) Actor that provides fast, reliable and customizable data analysis using your Google Analytics account. Perfect for businesses and marketers to unlock instant GA4 insights with flexible pay per event pricing. - **URL**: https://apify.com/smacient/ga4-mcp-worker.md - **Developed by:** [Tacheon Digital](https://apify.com/smacient) (community) - **Categories:** MCP servers, Integrations, SEO tools - **Stats:** 8 total users, 0 monthly users, 45.5% runs succeeded, 2 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 ## GA4 MCP Worker - Complete Usage Guide ### Overview A powerful Google Analytics 4 Model Context Protocol (MCP) Worker Actor that provides fast, reliable and customizable data analysis using Google Analytics APIs. Perfect for businesses and marketers to unlock instant GA4 insights with flexible pay-as-you-go pricing. ### Features - **Real-time and Historical Reporting:** Retrieve up-to-date and past GA4 data - **Comparative Period Analysis:** Analyze metrics over multiple date ranges - **Custom Dimensions & Metrics:** Run advanced, tailored queries for your business - **Account & Property Management:** List all your GA4 properties and accounts via MCP - **Tiered Pricing:** Volume discounts with transparent pay-as-you-go pricing - **MCP Integration:** Seamless Claude AI workflows ### Getting Started #### Step 1: Connect to Claude AI 1. Open Claude AI at [https://claude.ai](https://claude.ai) 2. Navigate to the integrations or MCP settings #### Step 2: Add MCP Connection Use the following MCP Connector URL: https://apify-ga4mcp.smacient.com/mcp Then you will be redirected to authenticate your google account associated to your GA4. #### Step 3: Authenticate with Google - Sign in using the **Google account associated with your GA4 properties** - This ensures secure and authorized access to your analytics data - Make sure you have appropriate permissions for the GA4 properties you want to access #### Step 4: Get Your Apify API Token 1. Visit your Apify Console 2. Navigate to Settings → Integrations 3. Copy your **Apify API token** 4. Paste this token when prompted during MCP setup #### Step 5: Start Using GA4 MCP Worker You're now ready to use the GA4 MCP Worker! Ask Claude to: - Run GA4 reports - Get real-time analytics - Create comparative reports - List properties and accounts ### Available Tools #### Core Reporting Tools | Tool | Description | Use Case | |------|-------------|----------| | `runReport` | Standard GA4 historical reports | Get pageviews, users, sessions for any date range | | `runRealtimeReport` | Live GA4 data | Monitor current website activity | | `runComparativeReport` | Compare two time periods | Month-over-month, year-over-year analysis | | `runReportWithOrdering` | Sorted reports | Top pages, top sources, ranked data | #### Management Tools | Tool | Description | Use Case | |------|-------------|----------| | `listAccounts` | List all GA4 accounts | Find your account IDs | | `listProperties` | List properties in an account | Get property IDs for reporting | | `getUserProperties` | Get all accessible properties | Quick overview of available data | # Actor input Schema ## `googleTokens` (type: `object`): Google OAuth tokens obtained from MCP server ## `toolName` (type: `string`): Name of the GA4 tool to execute ## `toolArgs` (type: `object`): Arguments for the selected tool ## Actor input object example ```json { "toolName": "getUserProperties", "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/ga4-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/ga4-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/ga4-mcp-worker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=smacient/ga4-mcp-worker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Analytics MCP (GA4)", "description": "A powerful Google Analytics 4 Model Context Protocol (MCP) Actor that provides fast, reliable and customizable data analysis using your Google Analytics account. Perfect for businesses and marketers to unlock instant GA4 insights with flexible pay per event pricing.", "version": "1.0", "x-build-id": "IjEHY8AtTPXGcnvS8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/smacient~ga4-mcp-worker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-smacient-ga4-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~ga4-mcp-worker/runs": { "post": { "operationId": "runs-sync-smacient-ga4-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~ga4-mcp-worker/run-sync": { "post": { "operationId": "run-sync-smacient-ga4-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": [ "runReport", "runRealtimeReport", "runComparativeReport", "runReportWithOrdering", "getUserProperties", "listProperties", "listAccounts" ], "type": "string", "description": "Name of the GA4 tool to execute", "default": "getUserProperties" }, "toolArgs": { "title": "Tool Arguments", "type": "object", "description": "Arguments for the selected tool", "properties": { "propertyId": { "title": "Property ID", "type": "string", "description": "GA4 Property ID (for report tools)" }, "metrics": { "title": "Metrics", "type": "array", "description": "Metrics to retrieve", "items": { "type": "string" } }, "dimensions": { "title": "Dimensions", "type": "array", "description": "Dimensions to group by", "items": { "type": "string" } }, "startDate": { "title": "Start Date", "type": "string", "description": "Start date (YYYY-MM-DD)" }, "endDate": { "title": "End Date", "type": "string", "description": "End date (YYYY-MM-DD)" }, "accountId": { "title": "Account ID", "type": "string", "description": "GA4 Account ID (for listProperties)" }, "orderBy": { "title": "Order By", "type": "string", "description": "Metric to order by (for runReportWithOrdering)" }, "period1Start": { "title": "Period 1 Start Date", "type": "string", "description": "First period start date" }, "period1End": { "title": "Period 1 End Date", "type": "string", "description": "First period end date" }, "period2Start": { "title": "Period 2 Start Date", "type": "string", "description": "Second period start date" }, "period2End": { "title": "Period 2 End Date", "type": "string", "description": "Second period end date" } }, "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 } } } } } } } } } } ```