# Metacritic Reviews Scraper (`sync-network/metacritic-reviews-scraper`) Actor Scrape user and critic reviews, scores, metadata, cast & crew from Metacritic for movies, TV shows, and games. Pure API — fast, reliable, no browser needed. Get review text, scores, sentiment, publication names, and full product details. Supports pagination, sentiment filtering, and sorting. - **URL**: https://apify.com/sync-network/metacritic-reviews-scraper.md - **Developed by:** [Alam](https://apify.com/sync-network) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## Metacritic Reviews Scraper Scrape reviews, scores, and detailed metadata from Metacritic for **movies**, **TV shows**, and **games**. ### What You Get - **Critic reviews** from professional publications (with scores 0-100, publication names, author, date) - **User reviews** with scores, review text, spoiler flags, and helpfulness votes - **Product metadata** including metascore, sentiment breakdown (positive/neutral/negative counts), genres, cast, crew, platforms, release dates, ratings, and more ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **URLs** | Array | — | Metacritic URLs to scrape (e.g. `https://www.metacritic.com/movie/the-matrix/`) | | **Slugs** | Array | — | Metacritic slugs if you don't have full URLs (e.g. `the-matrix`) | | **Content Types** | Select | Movies | Which types to check: Movies, TV Shows, Games | | **Max Reviews** | Integer | 100 | Maximum reviews per type (user or critic) per title | | **Include User Reviews** | Boolean | Yes | Scrape user-submitted reviews | | **Include Critic Reviews** | Boolean | Yes | Scrape professional critic reviews | | **Sentiment Filter** | Select | All | Filter by: All, Positive, Neutral, Negative | | **Sort Order** | Select | Most Recent | Sort by date or score | ### Output Each run produces two types of records: #### Product Records Contains title, metascore, critic review count, sentiment label, genres, cast, crew, platforms, release date, rating, and more. #### Review Records Contains review text, score, author, date, publication name (critics only), sentiment type, spoiler flag, and product reference. ### Use Cases - **Sentiment analysis** — aggregate positive/negative sentiment across hundreds of titles - **Competitive research** — track review scores and critic reception over time - **Content monitoring** — watch for new reviews on specific titles - **Data enrichment** — enrich movie/game databases with Metacritic scores and review counts - **Market analysis** — compare critic vs. audience reception across genres and platforms ### Tips - Use the **Sentiment Filter** to get only positive or negative reviews for targeted analysis - For games, reviews are available per-platform (PC, PlayStation, Xbox, etc.) - TV show pages include season counts and network information - The **Sort by Score** option is useful for finding the most polarizing reviews # Actor input Schema ## `urls` (type: `array`): List of Metacritic URLs to scrape. Supports movie, TV show, and game pages (e.g. https://www.metacritic.com/movie/the-matrix/). ## `slugs` (type: `array`): List of Metacritic slugs to scrape (e.g. 'the-matrix', 'baldurs-gate-3'). Use when you know the slug but don't have a full URL. All selected content types will be checked. ## `contentTypes` (type: `array`): Which content types to scrape when using slugs. Allowed values: movie, tv, game. ## `maxReviews` (type: `integer`): Maximum number of reviews (per type: user or critic) to scrape for each title. ## `includeUserReviews` (type: `boolean`): Whether to scrape user reviews. ## `includeCriticReviews` (type: `boolean`): Whether to scrape professional critic reviews. ## `sentimentFilter` (type: `string`): Filter reviews by sentiment. ## `sortOrder` (type: `string`): How to sort reviews. ## Actor input object example ```json { "urls": [ "https://www.metacritic.com/movie/the-matrix/" ], "slugs": [], "contentTypes": [ "movie" ], "maxReviews": 100, "includeUserReviews": true, "includeCriticReviews": true, "sentimentFilter": "all", "sortOrder": "date" } ```` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "urls": [ "https://www.metacritic.com/movie/the-matrix/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("sync-network/metacritic-reviews-scraper").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 = { "urls": ["https://www.metacritic.com/movie/the-matrix/"] } # Run the Actor and wait for it to finish run = client.actor("sync-network/metacritic-reviews-scraper").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 '{ "urls": [ "https://www.metacritic.com/movie/the-matrix/" ] }' | apify call sync-network/metacritic-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sync-network/metacritic-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Metacritic Reviews Scraper", "description": "Scrape user and critic reviews, scores, metadata, cast & crew from Metacritic for movies, TV shows, and games. Pure API — fast, reliable, no browser needed. Get review text, scores, sentiment, publication names, and full product details. Supports pagination, sentiment filtering, and sorting.", "version": "1.0", "x-build-id": "cuC0BXJ89ZvdLghzM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sync-network~metacritic-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sync-network-metacritic-reviews-scraper", "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/sync-network~metacritic-reviews-scraper/runs": { "post": { "operationId": "runs-sync-sync-network-metacritic-reviews-scraper", "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/sync-network~metacritic-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-sync-network-metacritic-reviews-scraper", "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", "properties": { "urls": { "title": "Metacritic URLs", "type": "array", "description": "List of Metacritic URLs to scrape. Supports movie, TV show, and game pages (e.g. https://www.metacritic.com/movie/the-matrix/).", "items": { "type": "string" } }, "slugs": { "title": "Metacritic Slugs", "type": "array", "description": "List of Metacritic slugs to scrape (e.g. 'the-matrix', 'baldurs-gate-3'). Use when you know the slug but don't have a full URL. All selected content types will be checked.", "default": [], "items": { "type": "string" } }, "contentTypes": { "title": "Content Types", "type": "array", "description": "Which content types to scrape when using slugs. Allowed values: movie, tv, game.", "default": [ "movie" ], "items": { "type": "string" } }, "maxReviews": { "title": "Maximum Reviews", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of reviews (per type: user or critic) to scrape for each title.", "default": 100 }, "includeUserReviews": { "title": "Include User Reviews", "type": "boolean", "description": "Whether to scrape user reviews.", "default": true }, "includeCriticReviews": { "title": "Include Critic Reviews", "type": "boolean", "description": "Whether to scrape professional critic reviews.", "default": true }, "sentimentFilter": { "title": "Sentiment Filter", "enum": [ "all", "positive", "neutral", "negative" ], "type": "string", "description": "Filter reviews by sentiment.", "default": "all" }, "sortOrder": { "title": "Sort Order", "enum": [ "date", "score" ], "type": "string", "description": "How to sort reviews.", "default": "date" } } }, "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 } } } } } } } } } } ```