# Steam Review Monitor: New Reviews Since Last Run (`accountable_eel/steam-review-monitor`) Actor

Steam review alerts by app ID: new reviews since your last run with recommendation, playtime, language and helpful votes. One row per review, no login, no key, no reviewer names. $1.50 per 1,000 reviews; a quiet run or a miss is free.

- **URL**: https://apify.com/accountable_eel/steam-review-monitor.md
- **Developed by:** [Adriana Voss](https://apify.com/accountable_eel) (community)
- **Categories:** Marketing, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.75 / 1,000 review returneds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Steam Review Monitor: New Reviews Since Last Run

**Get the Steam reviews that are new since your last run, for any game, by app ID.**
Each row is one review with the recommendation (thumbs up or down), the reviewer's playtime when they wrote it, the review language, the helpful and funny votes and the review text. Turn on monitoring and run it on a schedule: you only get, and only pay for, reviews posted since the previous run, and a quiet day is free. No login, no API key, no proxy. Reviewer names, Steam IDs and profile links are never returned.

**Who uses it:** studios and publishers watching sentiment after a patch or sale, community managers who want a review-bomb alert (set Recommendation to thumbs down), and market researchers tracking how a competitor's game is received.

**Related actors:** for a game's price, genres and release data by app ID use [Steam App Lookup](https://apify.com/accountable_eel/steam-app-lookup). This actor is reviews only.

### How to use

1. **In the Apify Console.** Open the actor page and click **Start** — the `appIds` field is already pre-filled with a working example. Results land in the run's dataset as soon as each item is found.
2. **Via the API.** Call it directly with a POST request — no Console needed once you have an API token:
   ```bash
   curl "https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
     -X POST \
     -H "Content-Type: application/json" \
     -d '{"appIds":["730"]}'
   ```
3. **On a schedule.** Save this actor as an Apify **Task** with the input you want, then add a **Schedule** (hourly, daily, weekly) so it runs on its own — no server of your own required.

### Input

```json
{
  "appIds": [
    "730"
  ]
}
```

One per line: the number in the game's store URL (730 for store.steampowered.com/app/730/) or the full store URL. Reads Steam's public review feed with no login and no key. Reviewer names and profile links are never returned. Accepted formats: 730, 570, https://store.steampowered.com/app/1245620/.

Apps are read one after another. Each app returns its newest reviews first (up to **Reviews per app**, 1 to 1,000). Language, Recommendation and the standard "only keep rows containing" words narrow the reviews before anything is remembered, so a review you filtered out is never counted as seen. With monitoring on, the run stops reading as soon as it reaches a review it already knows, so a quiet run is a single request per app. If more reviews arrive between two runs than **Reviews per app**, the run reports `truncated: true`; raise the cap or schedule more often.

The first monitoring run for an app returns its newest reviews and remembers them; from the next run on you only get reviews posted since. An unknown app ID comes back as a `found: false` row. A game with no reviews for your filters comes back as one row with `reviewCount: 0`. Neither is charged.

### Output

One row per review (the game's name, overall rating and all-time totals repeat on each row so a downstream sheet needs no join), for example:

| query | found | status | appId | appName | overallRating | totalReviews | totalPositive | totalNegative | positivePercent | monitorStatus | truncated | reviewCount | reviews | reviewId | recommended | reviewText | language | playtimeAtReviewHours | playtimeTotalHours | helpfulVotes | funnyVotes | weightedVoteScore | commentCount | steamPurchase | receivedForFree | writtenDuringEarlyAccess | primarilySteamDeck | createdAt | updatedAt | storeUrl | sourceUrl | isNew | firstSeenAt | scrapedAt |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 730 | true | OK | <steam app id> | <game name> | <overall rating> | <all-time reviews> | <all-time positive reviews> | <all-time negative reviews> | \<positive share (%)> | <monitoring status> | <more reviews were available> | <reviews returned> | \<all reviews (full list)> | <review id> | <recommended> | <review text> | <review language> | \<playtime when reviewed (h)> | \<playtime total (h)> | <helpful votes> | <funny votes> | <helpfulness score> | <comments> | <bought on steam> | <received for free> | <written in early access> | <mostly played on steam deck> | <posted on> | <last edited on> | <store page> | <source query url> | \<is this review new?> | <first seen on a run> | 1970-01-01T00:00:00.000Z |

A miss comes back as a row with `"found": false` and is never charged. `totalReviews`, `totalPositive` and `totalNegative` are `null` when Steam does not send them for the filter you chose, never `0`; `playtimeAtReviewHours` is `null` on older reviews that predate that field. `weightedVoteScore` is Steam's 0-1 helpfulness weight.

### Example runs

Paste any of these into the Input tab and press Start. All three return rows with no key, no token and no account of your own beyond an Apify login.

**The latest 20 reviews of Counter-Strike 2, every language.**

```json
{
  "appIds": ["730"],
  "maxReviews": 20
}
```

**Thumbs-down English reviews of two games, newest 50 each (a review-bomb check).**

```json
{
  "appIds": ["730", "570"],
  "language": "english",
  "reviewType": "negative",
  "maxReviews": 50
}
```

**A daily Steam review alert: only reviews posted since the last run.**

```json
{
  "appIds": ["https://store.steampowered.com/app/1245620/"],
  "maxReviews": 200,
  "onlyNewSinceLastRun": true,
  "deltaName": "elden-ring-daily"
}
```

### Pricing

- **Review returned**: $1.5 per 1,000 reviews

Plus a $0.005 start fee per run. Each event above is billed independently, only when it actually returns data — misses (`found:false`), games with no matching reviews and monitoring runs with nothing new are never charged.

### Use it from Clay, n8n, Make, or an AI agent

This actor runs synchronously over plain HTTP — call it directly from a script, a workflow tool, or an AI agent, no Apify Console needed once you have an API token.

```bash
curl "https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{"appIds":["730"]}'
```

**n8n.** Add an HTTP Request node: Method `POST`, URL `https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>`, Body Content Type `JSON`, JSON Body `{"appIds":["730"]}` (swap in an expression from an earlier node for a real value).

**Clay.** Add an "HTTP API" column: Method `POST`, URL `https://api.apify.com/v2/acts/accountable_eel~steam-review-monitor/run-sync-get-dataset-items?token=<YOUR_TOKEN>`, Body `{"appIds":["{{app ID}}"]}`, mapping the row's app ID into the `appIds` array.

**MCP.** In Claude, Cursor, or any MCP client with the Apify MCP server, ask for "steam reviews monitor by app id" — the agent will find and run this actor.

# Actor input Schema

## `appIds` (type: `array`):

One per line: the number in the game's store URL (730 for store.steampowered.com/app/730/) or the full store URL. Reads Steam's public review feed with no login and no key. Reviewer names and profile links are never returned. Accepted formats: 730, 570, https://store.steampowered.com/app/1245620/. You're only charged for the ones we actually find — a miss costs nothing.

## `maxReviews` (type: `integer`):

Newest reviews to read per app, 1 to 1,000 (Steam serves 100 per page). When monitoring, set it above how many reviews your game gets between two runs; the run reports truncated if it stopped at the cap.

## `testRun` (type: `boolean`):

Turn this on to test your input on a small sample before running the full list. Turn it off to process everything.

## `onlyFound` (type: `boolean`):

Only keep rows where something was actually found. Misses are always free, whether or not you show them here.

## `includeKeywords` (type: `array`):

Optional. Only keep results that mention at least one of these words (e.g. a job title, a city, a product name). Leave empty to keep everything.

## `excludeKeywords` (type: `array`):

Optional. Drop any result that mentions one of these words. Leave empty to skip nothing.

## `maxResults` (type: `integer`):

Optional. Stop the run once this many results have been found — useful for a quick, cheap sample. Leave blank for no limit.

## `language` (type: `string`):

Only reviews written in this language. Applied before monitoring remembers anything.

## `reviewType` (type: `string`):

Only recommended (positive) or only not-recommended (negative) reviews. Negative-only is the usual setting for a review-bomb alert.

## `onlyNewSinceLastRun` (type: `boolean`):

On: only reviews posted since your last run (per app) are returned and charged — run this on a schedule as a Steam review alert. Off: the newest reviews are returned every time. A quiet run costs only the start fee. The first monitoring run returns the newest reviews and remembers them.

## `deltaName` (type: `string`):

Optional. Name this watchlist's memory so two schedules stay apart, or keep one memory across a filter change. Leave empty to auto-derive one from your language, recommendation and keyword filters.

## `columns` (type: `array`):

Choose which pieces of information to include in each result row. All are included by default.

## `expandRows` (type: `boolean`):

When on, each review found gets its own row instead of being grouped under its app ID. You're still only charged once per app ID, no matter how many rows it produces.

## `maxConcurrency` (type: `integer`):

Parallel requests. Keep conservative — this target has no browser fallback, so getting blocked costs more than slow-and-steady.

## Actor input object example

```json
{
  "appIds": [
    "730"
  ],
  "maxReviews": 100,
  "testRun": false,
  "onlyFound": false,
  "includeKeywords": [],
  "excludeKeywords": [],
  "language": "all",
  "reviewType": "all",
  "onlyNewSinceLastRun": false,
  "deltaName": "",
  "columns": [
    "appId",
    "appName",
    "overallRating",
    "totalReviews",
    "totalPositive",
    "totalNegative",
    "positivePercent",
    "monitorStatus",
    "truncated",
    "reviewCount",
    "reviews",
    "reviewId",
    "recommended",
    "reviewText",
    "language",
    "playtimeAtReviewHours",
    "playtimeTotalHours",
    "helpfulVotes",
    "funnyVotes",
    "weightedVoteScore",
    "commentCount",
    "steamPurchase",
    "receivedForFree",
    "writtenDuringEarlyAccess",
    "primarilySteamDeck",
    "createdAt",
    "updatedAt",
    "storeUrl",
    "sourceUrl",
    "isNew",
    "firstSeenAt"
  ],
  "expandRows": true,
  "maxConcurrency": 1
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "appIds": [
        "730"
    ],
    "includeKeywords": [],
    "excludeKeywords": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("accountable_eel/steam-review-monitor").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "appIds": ["730"],
    "includeKeywords": [],
    "excludeKeywords": [],
}

# Run the Actor and wait for it to finish
run = client.actor("accountable_eel/steam-review-monitor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "appIds": [
    "730"
  ],
  "includeKeywords": [],
  "excludeKeywords": []
}' |
apify call accountable_eel/steam-review-monitor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,accountable_eel/steam-review-monitor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/PQ7nYU5GvuugtrFI4/builds/CuHxB09NVHTtYWuID/openapi.json