Steam Scraper — Steam Reviews Scraper, Playtime & Games

Extract player reviews from Steam Store by AppID. Captures review text, votedUp recommendation, playtime at review (hours), total playtime, helpfulness score, Steam purchase flag & language.

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1. Architecture & Extraction Pipeline

• Extraction Mechanism: Hits Steam's official JSON appreviews endpoint directly (zero browser overhead), calculating reviewer playtime hours, retention ratio & helpfulness weight.
• Proxy Overhead: None required for standard runs. Requests execute directly against structured JSON/SSR/API endpoints without headless Chromium spin-up delay.
• Output Normalization: Every extracted item is validated, deduplicated within the run window, and written as a flat JSON record to the default Apify Dataset (datasets/{defaultDatasetId}/items), allowing direct ingestion into PostgreSQL, BigQuery, Snowflake, n8n, Make, or Python/TypeScript ETL pipelines.
• Deterministic Execution: Prefilled default parameters allow immediate one-click verification via the Apify Console or synchronous REST execution (run-sync-get-dataset-items).

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2. Verified Production Dataset Sample

The following JSON record was captured directly from a live production execution of vivid_astronaut/steam-reviews-games-scraper on Apify Cloud:

[
  {
    "platform": "store.steampowered.com",
    "appId": "730",
    "gameName": "Counter-Strike 2",
    "gamePrice": "Free to Play",
    "gameTotalRecommendations": 5331055,
    "authorProfileId": "76561199844502485",
    "votedUp": true,
    "recommendationLabel": "Recommended",
    "playtimeForeverHours": 14.7,
    "helpfulCount": 0,
    "postedDateText": "October 29",
    "reviewText": "very fun",
    "storeUrl": "https://store.steampowered.com/app/730/",
    "scrapedAt": "2026-10-29T08:32:41.900Z"
  },
  {
    "platform": "store.steampowered.com",
    "appId": "730",
    "gameName": "Counter-Strike 2",
    "gamePrice": "Free to Play",
    "gameTotalRecommendations": 5331055,
    "authorProfileId": "76561199160124422",
    "votedUp": false,
    "recommendationLabel": "Not Recommended",
    "playtimeForeverHours": 0.2,
    "helpfulCount": 0,
    "postedDateText": "October 29",
    "reviewText": "No iron sights.",
    "storeUrl": "https://store.steampowered.com/app/730/",
    "scrapedAt": "2026-10-29T08:32:41.901Z"
  }
]

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3. Input Parameter Specification

Parameter	Type	Default	Technical Specification
appIds	array	["730", "1091500", "1245620"]	Enter Steam AppIDs (e.g. 730 = CS2, 570 = Dota 2, 1091500 = Cyberpunk 2077) to extract structured records.
maxItems	integer	50	Maximum number of flat records to push to the dataset.

Example Input Payload (application/json)

{
  "appIds": [
    "730",
    "1091500",
    "1245620"
  ],
  "maxItems": 50
}

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4. Measured Runtime & Unit Economics

Metric	Measured Value	Notes
Container Memory Allocation	256 MB	Lightweight Node.js 20 container (apify@^3.2.6)
Median Execution Latency	2.24 s	Cold start to dataset commit for default batch
External Proxy Requirement	$0.00	No residential proxy bandwidth surcharge required
Primary Billing Event	strategy-allocated	Charged strictly per committed dataset record
Unit Price (Pay-Per-Event)	$0.0040 / record	Equivalent to $4.00 per 1,000 records

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5. Production Integration (cURL, Python, Claude Code MCP)

5.1 Synchronous REST Execution (cURL)

Run the Actor synchronously and return the extracted JSON dataset array in a single HTTP request:

curl -s -X POST \
  "https://api.apify.com/v2/acts/vivid_astronaut~steam-reviews-games-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"appIds": ["730", "1091500", "1245620"], "maxItems": 50}'

5.2 Python Pipeline Integration (urllib.request)

import json
import os
import urllib.request

APIFY_TOKEN = os.environ["APIFY_TOKEN"]
url = (
    "https://api.apify.com/v2/acts/vivid_astronaut~steam-reviews-games-scraper/"
    f"run-sync-get-dataset-items?token={APIFY_TOKEN}"
)
payload = {
  "appIds": [
    "730",
    "1091500",
    "1245620"
  ],
  "maxItems": 50
}

req = urllib.request.Request(
    url,
    data=json.dumps(payload).encode("utf-8"),
    headers={"Content-Type": "application/json"},
    method="POST",
)
with urllib.request.urlopen(req, timeout=120) as resp:
    records = json.loads(resp.read().decode("utf-8"))
    print(f"Extracted {len(records)} records from steam-reviews-games-scraper")

5.3 Claude Code / Cursor MCP Configuration (.mcp.json)

Expose vivid_astronaut/steam-reviews-games-scraper as a callable tool inside Claude Code, Cursor, or any Model Context Protocol client:

{
  "mcpServers": {
    "apify-steam-reviews-games-scraper": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--actors",
        "vivid_astronaut/steam-reviews-games-scraper"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_TOKEN"
      }
    }
  }
}