# Steam Store Search — review scores you can actually rank by (`entrogix_works/steam-store-search`) Actor

Search the Steam store and get one row per game: price and discount in a currency you choose, release date, platforms, and the review score together with the review count and a Wilson lower bound — so a 100%-positive game with 12 reviews stops outranking a 92% game with 40,000. Unofficial.

- **URL**: https://apify.com/entrogix\_works/steam-store-search.md
- **Developed by:** [Entrogix Works](https://apify.com/entrogix_works) (community)
- **Categories:** E-commerce, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$3.00 / 1,000 game scrapeds

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Steam Store Search — review scores you can actually rank by

Search the Steam store and get one row per game, with the two things store listings make hard:
a price you can compare between runs, and a review score you can sort on without being fooled.

### The review score problem

Steam shows "Very Positive". So does a game with 12 reviews and a game with 40,000.
Sorting on the raw positive percentage puts the 12-review game first, every time.

Every row therefore carries all four pieces:

| Field | What it is |
|---|---|
| `reviewSummary` | Steam's own label — "Very Positive", "Mixed", … |
| `positivePercent` | The raw percentage. Do not sort on this |
| `reviewCount` | How many reviews that percentage is built on |
| **`positiveLowerBound`** | The Wilson 95% lower bound on the positive rate. **This is the one to sort on** |
| `reviewsEnoughToRank` | `true` at 30 reviews or more |

A game at 88% from 72 reviews has a lower bound of 77.9%. A game at 84% from 2,622 reviews has
82.5%. The second one is the safer bet, and only the lower bound says so.

**The score is language-scoped.** Steam calculates it from reviews written in the viewer's
language — its own tooltip says as much. This actor pins the language to English on every request
so the number means the same thing on every run. It is *English-language reviews*, not all reviews.

### The price problem

Steam prices differ by region, and without a region pinned they follow wherever the request came
from. The same search returned `$11.99`, `¥1,300` and `11,99€` on 2026-08-23. Pick a
**Price region** and every row comes back in that currency, with `currency` and `country`
attached so nothing gets summed across regions by accident.

Three states are kept apart rather than collapsed into a number:

- **A price** — `price` is a number, `currency` is set
- **Free to play** — `isFree` is `true`, `price` is `0`
- **No price yet** — `priceUnavailable` is `true` and `price` is **`null`**, not `0`.
  Pre-release and region-unavailable titles land here. In one 50-result sample, 22 of them did.
  If they were `0`, they would fill the top of any cheapest-first sort.

### Fields

`appId`, `title`, `url` (tracking parameters stripped), `releasedText` / `releasedAt`
(ISO date, or `null` for "Coming soon" and "To be announced"), `platforms`
(`win` / `mac` / `linux`), the five review fields above, `price`, `priceText`,
`originalPriceText`, `discountPercent`, `currency`, `country`, `isFree`, `priceUnavailable`.

### Input

```json
{
  "term": "roguelike",
  "country": "us",
  "sort": "reviews",
  "onlyDiscounted": false,
  "maxItems": 50
}
```

Results come 25 per page. Rankings shift while a run is in progress, so rows are de-duplicated
by `appId` — a run asking for 50 returns 50 distinct games, not 50 slots.

### Output

One dataset row per game, plus a `SEARCH_SUMMARY` record: how many games had enough reviews to
rank, how many have no price yet, how many are discounted and the deepest discount, the price
region used, and the currencies actually seen — if more than one shows up, the region setting
did not take, and you should know that rather than find it in the data later.

### Pacing

Requests are serialised with a minimum interval of 3 seconds. Please do not lower `minIntervalMs`.

Unofficial. Not affiliated with Valve or Steam.

# Actor input Schema

## `term` (type: `string`):

Words to match. Leave empty to walk the whole catalogue in the chosen order.

## `country` (type: `string`):

Steam prices differ by region. Without this, prices would follow whatever region the run happens to come from, and two runs would not be comparable.

## `sort` (type: `string`):

How the store orders results. 'Most reviewed' surfaces established titles; 'Newest releases' is what you want for tracking what just shipped.

## `onlyDiscounted` (type: `boolean`):

Restrict to titles with an active discount right now.

## `maxItems` (type: `integer`):

Results come 25 per page. You are charged per game returned.

## `minIntervalMs` (type: `integer`):

Pacing between page fetches. Do not lower this.

## `useProxy` (type: `boolean`):

Off by default. Turn on only if you see blocks.

## `proxyType` (type: `string`):

Only used when the proxy is enabled.

## Actor input object example

```json
{
  "term": "roguelike",
  "country": "us",
  "sort": "relevance",
  "onlyDiscounted": false,
  "maxItems": 50,
  "minIntervalMs": 3000,
  "useProxy": false,
  "proxyType": "DATACENTER"
}
```

# Actor output Schema

## `results` (type: `string`):

Every game this run produced, as JSON.

## `summary` (type: `string`):

How many games had enough reviews to rank, how many had no price yet, how many are discounted, and the price region used.

# 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 = {
    "term": "roguelike"
};

// Run the Actor and wait for it to finish
const run = await client.actor("entrogix_works/steam-store-search").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 = { "term": "roguelike" }

# Run the Actor and wait for it to finish
run = client.actor("entrogix_works/steam-store-search").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 '{
  "term": "roguelike"
}' |
apify call entrogix_works/steam-store-search --silent --output-dataset

```

## MCP server setup

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

```

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/FPjsah2cVpXNP2VZG/builds/94MHeiH49qBZ77elu/openapi.json