# SERP + PAA + AI Overview Content Gap Pack (`isotonic/serp-paa-ai-overview-content-gap-pack`) Actor Fetches public Google SERP HTML, extracts organic/PAA/related-search signals, and builds agency-ready content gap packs per keyword. - **URL**: https://apify.com/isotonic/serp-paa-ai-overview-content-gap-pack.md - **Developed by:** [Brian Keefe](https://apify.com/isotonic) (community) - **Categories:** SEO tools, AI, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## serp-paa-ai-overview-content-gap-pack Production-ready Apify Actor for building a narrow, agency-report-ready SERP content gap pack from public Google HTML. For each keyword, the actor: - Fetches public Google SERP HTML with Crawlee/Cheerio - Extracts visible organic results with `title`, `url`, `snippet`, `position`, and `domain` - Extracts visible People Also Ask style questions when present - Extracts related searches when present - Detects AI Overview presence/signals on a best-effort basis only - Produces a content gap pack with target rankings, pages to update, competitor clusters, topic opportunities, and evidence ### Input Example: ```json { "keywords": ["content gap analysis", "seo content audit"], "targetDomain": "example.com", "country": "us", "language": "en", "resultLimit": 10, "maxConcurrency": 2, "requestTimeoutSecs": 30, "maxRequestRetries": 2 } ```` See `examples/sample-input.json`. ### Output The actor writes one content gap pack per keyword to the Apify Dataset. Each record includes: - `reportMetadata` - `serp.organicResults` - `serp.paaQuestions` - `serp.relatedSearches` - `serp.aiOverview` with `bestEffort: true` - `targetDomainRankings` - `pagesToUpdate` - `competitorClusters` - `contentGaps` - `evidence` See `examples/sample-output.json`. ### AI Overview Note `aiOverview` detection is explicitly best-effort. Google markup changes frequently, and AI Overview modules are not consistently represented in static HTML. Treat these signals as indicative, not guaranteed. ### Local Development Install dependencies: ```bash npm install ``` Run the actor locally with Apify local storage: ```bash npm start ``` Run deterministic tests with fixture HTML only: ```bash npm test ``` Run deterministic smoke verification with fixture HTML served locally: ```bash npm run smoke ``` Run with Apify CLI: ```bash npx --yes apify-cli run ``` ### Notes - The runtime actor can fetch live public SERP HTML. - The included tests and smoke script never depend on live Google responses. - This actor intentionally focuses on a small, report-ready SERP analysis workflow rather than raw SERP JSON export. # Actor input Schema ## `keywords` (type: `array`): Keywords to query on Google and convert into report-ready content gap packs. ## `targetDomain` (type: `string`): Domain to benchmark against the visible SERP results, such as example.com. ## `country` (type: `string`): Google country code for gl. Example: us, gb, au. ## `language` (type: `string`): Google language code for hl. Example: en, fr, de. ## `resultLimit` (type: `integer`): Maximum organic results to capture per keyword. ## `maxConcurrency` (type: `integer`): Crawler concurrency for SERP fetches. Keep low to be respectful. ## `requestTimeoutSecs` (type: `integer`): Per-request timeout in seconds. ## `maxRequestRetries` (type: `integer`): How many times a failed SERP request should be retried. ## `serpBaseUrl` (type: `string`): Advanced override for local smoke tests or controlled environments. Defaults to Google search. ## Actor input object example ```json { "keywords": [ "content gap analysis", "seo content audit" ], "targetDomain": "example.com", "country": "us", "language": "en", "resultLimit": 10, "maxConcurrency": 2, "requestTimeoutSecs": 30, "maxRequestRetries": 2, "serpBaseUrl": "https://www.google.com/search" } ``` # 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 = { "keywords": [ "content gap analysis", "seo content audit" ], "targetDomain": "example.com", "country": "us", "language": "en", "serpBaseUrl": "https://www.google.com/search" }; // Run the Actor and wait for it to finish const run = await client.actor("isotonic/serp-paa-ai-overview-content-gap-pack").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 = { "keywords": [ "content gap analysis", "seo content audit", ], "targetDomain": "example.com", "country": "us", "language": "en", "serpBaseUrl": "https://www.google.com/search", } # Run the Actor and wait for it to finish run = client.actor("isotonic/serp-paa-ai-overview-content-gap-pack").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 '{ "keywords": [ "content gap analysis", "seo content audit" ], "targetDomain": "example.com", "country": "us", "language": "en", "serpBaseUrl": "https://www.google.com/search" }' | apify call isotonic/serp-paa-ai-overview-content-gap-pack --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=isotonic/serp-paa-ai-overview-content-gap-pack", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SERP + PAA + AI Overview Content Gap Pack", "description": "Fetches public Google SERP HTML, extracts organic/PAA/related-search signals, and builds agency-ready content gap packs per keyword.", "version": "0.1", "x-build-id": "2ZPCZtOQhH4jVTvK8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/isotonic~serp-paa-ai-overview-content-gap-pack/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-isotonic-serp-paa-ai-overview-content-gap-pack", "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/isotonic~serp-paa-ai-overview-content-gap-pack/runs": { "post": { "operationId": "runs-sync-isotonic-serp-paa-ai-overview-content-gap-pack", "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/isotonic~serp-paa-ai-overview-content-gap-pack/run-sync": { "post": { "operationId": "run-sync-isotonic-serp-paa-ai-overview-content-gap-pack", "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": [ "keywords", "targetDomain" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Keywords to query on Google and convert into report-ready content gap packs.", "items": { "type": "string" } }, "targetDomain": { "title": "Target Domain", "type": "string", "description": "Domain to benchmark against the visible SERP results, such as example.com." }, "country": { "title": "Country", "type": "string", "description": "Google country code for gl. Example: us, gb, au.", "default": "us" }, "language": { "title": "Language", "type": "string", "description": "Google language code for hl. Example: en, fr, de.", "default": "en" }, "resultLimit": { "title": "Result Limit", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum organic results to capture per keyword.", "default": 10 }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 5, "type": "integer", "description": "Crawler concurrency for SERP fetches. Keep low to be respectful.", "default": 2 }, "requestTimeoutSecs": { "title": "Request Timeout Seconds", "minimum": 5, "maximum": 120, "type": "integer", "description": "Per-request timeout in seconds.", "default": 30 }, "maxRequestRetries": { "title": "Max Request Retries", "minimum": 0, "maximum": 5, "type": "integer", "description": "How many times a failed SERP request should be retried.", "default": 2 }, "serpBaseUrl": { "title": "SERP Base URL", "type": "string", "description": "Advanced override for local smoke tests or controlled environments. Defaults to Google search.", "default": "https://www.google.com/search" } } }, "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 } } } } } } } } } } ```