# GEO Content Auditor — AI Answerability Score (`muhammadafzal/geo-content-auditor`) Actor Audit any webpage for AI-answerability. Get a 0-100 score per page and a prioritized fix list: what to add, restructure, or mark up so ChatGPT, Perplexity, Claude, and Gemini cite your content in answers. - **URL**: https://apify.com/muhammadafzal/geo-content-auditor.md - **Developed by:** [Muhammad Afzal](https://apify.com/muhammadafzal) (community) - **Categories:** AI, SEO tools, MCP servers - **Stats:** 0 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 page auditeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## GEO Content Auditor — AI Answerability Score **Audit any webpage for AI-answerability.** Get a 0–100 score per page and a prioritized fix list so ChatGPT, Perplexity, Claude, and Gemini start citing your content in answers. This is the "fix your own pages" piece of the GEO family: the **AI Visibility Rank Tracker** tells you *if* you're being mentioned; the **AI Citation Monitor** shows *which sources* AI cites; the **GEO Content Auditor** tells you *why your page isn't being picked* and exactly what to change. --- ### What it audits Each page is scored across 5 dimensions (100 pts total): | Dimension | Max | What it checks | |---|---|---| | **Structure** | 30 | Question-style headings, direct answer near top, FAQ sections, lists/tables, H1-H3 hierarchy | | **Extractable facts** | 25 | Specific stats, dates, named entities, clear definitions, concrete examples | | **Entity clarity** | 20 | Brand clearly defined, author byline, JSON-LD structured data, product clearly named | | **Machine readability** | 15 | Semantic markup, content chunkability, `llms.txt` presence | | **Authority signals** | 10 | Sources cited, publication date, internal links, breadcrumbs | --- ### Output per page ```json { "url": "https://example.com/blog/best-tools", "page_title": "Best Productivity Tools 2026", "ai_answerability_score": 61, "grade": "C", "structure_score": 18, "facts_score": 15, "entity_score": 12, "readability_score": 10, "authority_score": 6, "strengths": ["Clear H2 headings", "Includes specific statistics"], "issues": [ "No FAQ schema — add FAQPage JSON-LD to become directly quotable", "No direct 1-sentence answer in opening paragraph", "Brand name not defined clearly — add Organization JSON-LD" ], "quick_wins": [ "Add FAQPage JSON-LD with 5 buyer questions", "Open with a direct 1-sentence answer", "Add publication and last-updated dates" ], "missing_schema_types": ["FAQPage", "Organization"], "suggested_qa_pairs": [ { "question": "What is the best productivity tool for teams?", "answer_hint": "Compare top 3 with use cases" } ], "llms_txt_present": false, "has_json_ld": false, "word_count": 1240, "prompt_fit_score": 55, "error": null, "audited_at": "2026-06-08T12:00:00.000Z" } ```` Plus a **SUMMARY** in the key-value store: site-wide average score, top issues across all pages, quick wins, priority pages (lowest scoring = biggest opportunity), and a missing schema frequency table. *** ### Input | Field | Type | Description | |---|---|---| | `startUrls` | URL list | Pages to audit. | | `brandName` | string | Your brand/product name (entity clarity check). Default: `Apify`. | | `targetPrompts` | string\[] | Buyer questions this content should answer. Optional — scores prompt-fit when provided. | | `maxPages` | integer | Max pages per run (1–50). Default: `10`. | The LLM model is managed internally for predictable cost and quality. #### Example input ```json { "startUrls": [ { "url": "https://yoursite.com/blog/best-tools" }, { "url": "https://yoursite.com/product/features" } ], "brandName": "YourBrand", "targetPrompts": [ "What is the best tool for X?", "How do I solve Y?" ], "maxPages": 10 } ``` *** ### API key (required) All LLM analysis runs through **[OpenRouter](https://openrouter.ai)** — set one environment variable in the Apify Console (Settings → Environment variables, mark **Secret**): ``` OPENROUTER_API_KEY = sk-or-... ``` *** ### Pricing (Pay Per Event) | Event | When | |---|---| | `actor-start` | Once per run. | | `page-audited` | Each successfully audited page. | Failed pages are **not charged**. *** ### Use cases - **GEO content strategy** — find the exact structural and semantic gaps keeping your pages out of AI answers. - **Content audit before publishing** — score new content before it goes live. - **Agency client reporting** — deliver an AI-readiness score with actionable fixes. - **`llms.txt` gap detection** — instantly see which of your domains are missing it. - **Pairs with AI Visibility Rank Tracker + AI Citation Monitor** for the full GEO picture: are you cited? → what do AI engines cite? → why aren't your pages being picked? # Actor input Schema ## `startUrls` (type: `array`): Pages to audit for AI-answerability. Each URL is fetched and scored. ## `brandName` (type: `string`): Your brand or product name. Used to check entity clarity on each page. ## `targetPrompts` (type: `array`): Buyer questions this content should answer (e.g. 'What is the best web scraping tool?'). Leave empty to skip prompt-fit scoring. ## `maxPages` (type: `integer`): Maximum number of pages to audit per run. ## `model` (type: `string`): OpenRouter model slug used for page analysis. ## Actor input object example ```json { "startUrls": [ { "url": "https://apify.com/about" } ], "brandName": "Apify", "targetPrompts": [], "maxPages": 10, "model": "openai/gpt-4o-mini" } ``` # Actor output Schema ## `results` (type: `string`): Link to the dataset containing all page audit records. # 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 = { "startUrls": [ { "url": "https://apify.com/about" } ], "brandName": "Apify", "targetPrompts": [] }; // Run the Actor and wait for it to finish const run = await client.actor("muhammadafzal/geo-content-auditor").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 = { "startUrls": [{ "url": "https://apify.com/about" }], "brandName": "Apify", "targetPrompts": [], } # Run the Actor and wait for it to finish run = client.actor("muhammadafzal/geo-content-auditor").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 '{ "startUrls": [ { "url": "https://apify.com/about" } ], "brandName": "Apify", "targetPrompts": [] }' | apify call muhammadafzal/geo-content-auditor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=muhammadafzal/geo-content-auditor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GEO Content Auditor — AI Answerability Score", "description": "Audit any webpage for AI-answerability. Get a 0-100 score per page and a prioritized fix list: what to add, restructure, or mark up so ChatGPT, Perplexity, Claude, and Gemini cite your content in answers.", "version": "1.0", "x-build-id": "v1gicmL6h63jQ0ALs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/muhammadafzal~geo-content-auditor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-muhammadafzal-geo-content-auditor", "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/muhammadafzal~geo-content-auditor/runs": { "post": { "operationId": "runs-sync-muhammadafzal-geo-content-auditor", "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/muhammadafzal~geo-content-auditor/run-sync": { "post": { "operationId": "run-sync-muhammadafzal-geo-content-auditor", "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": [ "startUrls" ], "properties": { "startUrls": { "title": "URLs to Audit", "type": "array", "description": "Pages to audit for AI-answerability. Each URL is fetched and scored.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "brandName": { "title": "Brand / Product Name", "type": "string", "description": "Your brand or product name. Used to check entity clarity on each page." }, "targetPrompts": { "title": "Target AI Prompts (optional)", "type": "array", "description": "Buyer questions this content should answer (e.g. 'What is the best web scraping tool?'). Leave empty to skip prompt-fit scoring.", "items": { "type": "string" } }, "maxPages": { "title": "Max Pages", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of pages to audit per run.", "default": 10 }, "model": { "title": "LLM Model", "type": "string", "description": "OpenRouter model slug used for page analysis.", "default": "openai/gpt-4o-mini" } } }, "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 } } } } } } } } } } ```