Metacritic Reviews & Scores Scraper

Extract public Metacritic title scores, review counts, release details and optional review snippets for movies, TV, games and music. Give this Metacritic scraper title URLs, a search term or a browse page, then export title-level rows as JSON, CSV or Excel.

Review snippets are bounded samples nested in each title, not a complete review archive. Coverage depends on the public pages available during the run.

Quick start

1. Open the Actor's Input tab.
2. Paste the input below for one known title. Clear any prefilled query to avoid adding a search.
3. Start the Actor, then open its dataset in Storage and export the results.

{
  "startUrls": [{ "url": "https://www.metacritic.com/movie/the-godfather/" }],
  "query": "",
  "maxItems": 1,
  "includeReviews": false
}

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose a public example to see its settings before starting a run:

• Scrape a Metacritic movie ranking page
• Search Metacritic games and collect review snippets
• Scrape one Metacritic movie score page

What you get

One row per discovered title with its public URL, media type, name and available critic and user scores. Release details, genres, platform or network labels and a synopsis can accompany it. Optional critic and user snippets remain small nested samples rather than separate records or a full review archive.

Who is it for?

• Catalog teams enriching known titles with source-linked metadata and scores.
• Reception researchers comparing available critic and user assessments.
• Entertainment editors reviewing candidates from public browse and search pages.
• Text analysts examining small exposed review samples in title context.

How you can use the data

• Enrich a known movie entry. Use the existing Godfather detail URL with a one-title cap, query cleared and snippets off. Join the known URL to your catalog while keeping critic score and user score separate. Missing scores are unknown rather than zero.
• Read a small game review sample. Use the existing elden ring query with two titles, snippets enabled and one snippet per review type. Retain source, reviewType, quotation and link for close reading without treating those snippets as representative of every review.
• Browse a selected category deliberately. Clear URLs and query to use the existing game category recipe. For a specific ranking or year, supply the actual browse URL yourself. The unused sort and year controls do not create those filters.
• Compare your own reception snapshots. Keep URL and fetchedAt alongside scores and review counts when joining later exports. Direct detail records normally lack rankingContext, while discovered titles can retain listing context. The Actor does not apply its own score sorting or maintain a title history.

Input settings

Setting	API key	Behavior
Start URLs	startUrls	Public Metacritic browse, search or title detail URLs.
Search query	query	Adds a search alongside supplied URLs. Clear URLs for search only.
Category	category	movie (default), tv, game or music. Used only with no URLs and no query.
Browse sort context	browseSort	Accepted but unused. Does not sort results or change ranking context.
Year context	year	Accepted but unused. Does not filter results.
Maximum titles	maxItems	Maximum saved titles, 1–1,000. Default 5.
Include review snippets	includeReviews	Optional public critic/user snippets. Default false.
Review snippets per type	maxReviewSnippets	0–10 per review type per title. Default 3.
Proxy configuration	proxyConfiguration	Existing Apify Proxy default, custom proxies or explicit no-proxy settings. See field help.

For specific rankings or year selections, supply the exact Metacritic browse URL. The Actor inspects supplied listing pages, not an exhaustive catalog. Input help explains connection settings in detail.

Example inputs

Search for a title and sample review snippets

{
  "startUrls": [],
  "query": "elden ring",
  "maxItems": 2,
  "includeReviews": true,
  "maxReviewSnippets": 1
}

Browse games without adding a search

{
  "startUrls": [],
  "query": "",
  "category": "game",
  "maxItems": 5,
  "includeReviews": false
}

Output data

Each dataset row represents a discovered title. The following is an illustrative shape, not a new extraction result:

{
  "url": "https://www.metacritic.com/movie/the-godfather/",
  "contentType": "movie",
  "name": "The Godfather",
  "releaseDate": null,
  "platformsNetwork": [],
  "genres": [],
  "metascore": null,
  "userScore": null,
  "criticReviewCount": null,
  "userReviewCount": null,
  "summary": null,
  "rankingContext": null,
  "reviewSnippets": [],
  "fetchedAt": "2026-10-25T00:00:00.000Z"
}

Fields	Meaning
url, contentType, name	Title URL, media type and name.
releaseDate, genres, platformsNetwork	Available release, genre and game-platform/TV-network details.
metascore, userScore	Available critic score and user score. Missing values are null.
criticReviewCount, userReviewCount	Counts detected on the page. Availability and labels vary.
summary	Available synopsis or description.
rankingContext	Listing rank/search context when discovered. Direct title URLs normally have null.
reviewSnippets	Nested samples with source, author, score, quote, reviewType and url. Individual values may be missing.
fetchedAt	Collection timestamp.

A best-effort RUN-SUMMARY key-value record contains saved/failed/skipped/remaining counters, pending URLs and a completion timestamp. It is not a complete per-URL error report.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

The start event is charged once when a validated run begins, including runs with no saved titles. The result event applies to each title record produced. Review snippets are nested in title rows and are not charged as separate review results.

Keep the title cap small and leave snippets off when only scores are needed. See the live Pricing tab for current rates and discounts.

Tips for best results

• Start with one known detail URL and clear the query. URLs and a nonempty query otherwise add work together.
• Keep critic and user scores distinct. Missing values do not mean zero reception.
• Use the actual browse URL for a ranking or year. The accepted sort and year inputs do not change collection.
• Cap snippets separately for each review type. Available quotations are samples, not full review histories.
• Inspect summary counters and dataset records together. The best-effort summary is not a complete per-URL error report.

Limits and caveats

Only public Metacritic pages are supported. This is an independent scraper rather than an official Metacritic API, with no private or authenticated account access. Public page changes and access restrictions can limit coverage.

Supplied URLs and a nonempty query contribute combined work. Category applies only with no URLs or query, while browseSort and year are accepted but unused. Use an exact browse URL for specific ranking context or year selection. Only inspected listing pages contribute discovered titles, without exhaustive catalog coverage or Actor-applied score sorting.

Scores, counts, release fields and snippet details can be missing. Critic and user scores are separate and absent scores remain null. Direct detail URLs normally lack ranking context. Snippets require the option enabled and a positive per-type cap, and an empty array does not establish that no reviews exist. They are bounded nested excerpts rather than complete review exports.

Snippet collection adds work and each review type has its own limit. Unavailable details may be skipped while useful title rows remain. Listing failures or no discovered title URLs can fail a run. Successful status alone does not establish complete extraction or every optional field's availability.

RUN-SUMMARY contains best-effort saved, failed, skipped and remaining counters, pending URLs and completion time. It is not a full per-URL error report. Perform catalog joins, score comparisons and repeated-snapshot analysis in your own destination rather than treating the export as a maintained history.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~metacritic-reviews-scores-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data @- <<'JSON'
{
  "startUrls": [
    {
      "url": "https://www.metacritic.com/movie/the-godfather/"
    }
  ],
  "query": "",
  "maxItems": 1,
  "includeReviews": false
}
JSON

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/metacritic-reviews-scores-scraper').call({
  "startUrls": [
    {
      "url": "https://www.metacritic.com/movie/the-godfather/"
    }
  ],
  "query": "",
  "maxItems": 1,
  "includeReviews": false
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/metacritic-reviews-scores-scraper').call(run_input={'startUrls': [{'url': 'https://www.metacritic.com/movie/the-godfather/'}], 'query': '', 'maxItems': 1, 'includeReviews': False})
print(client.dataset(run['defaultDatasetId']).list_items().items)

MCP and AI agents

You can ask your AI assistant to collect data with Metacritic Reviews & Scores Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Metacritic Reviews & Scores Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/metacritic-reviews-scores-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Metacritic Reviews & Scores Scraper by Fetch Cat, or give it the identifier fetch_cat/metacritic-reviews-scores-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Use Fetch Cat’s Metacritic Reviews and Scores Scraper to retrieve https://www.metacritic.com/movie/the-godfather/ with the query cleared, a one-title cap and snippets off. Keep critic and user scores distinct and mark missing values as unknown.”
• “Collect up to 2 titles from an elden ring Metacritic search with start URLs empty and one critic and one user snippet per title where exposed. Keep quotation links and review types without claiming a complete review sample.”
• “Retrieve up to 5 Metacritic game titles with start URLs and query cleared, using the game category and snippets off. Keep title URLs, release fields and available scores without applying an invented ranking or year filter.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I find the highest-rated Metacritic games?

Supply the relevant Metacritic game ranking URL. The Actor collects titles from that page. It does not apply its own score sorting or guarantee that every ranked title is returned. browseSort does not select an ordering.

Can I compare critic and user review scores?

Yes, where both scores are exposed. metascore and userScore are distinct fields, and neither is guaranteed for every title. A missing score does not mean zero.

Why are snippets empty or results incomplete?

Snippets require includeReviews: true and a positive maxReviewSnippets. Public review text can be unavailable, and page changes or access failures can limit output. Try one known public detail URL first. Do not treat an empty array as proof that a title has no reviews.

Is this a Metacritic API or full review export?

This is an independent scraper of public pages, not an official Metacritic API. It returns title records with bounded nested snippets, not complete review histories, private data or authenticated account content.

Related Actors

• Steam Store Games Scraper — Enrich game catalogs with store metadata.
• Steam Reviews Scraper — Research public player review text.
• Google News Scraper — Find news coverage of releases and titles.
• Google Trends Scraper — Explore current trending searches by country.
• YouTube Search Results Scraper — Discover public videos and channels for entertainment research.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥