# 📖 Wikipedia Scraper - Structured Knowledge & Article Extractor (`unrivaled_fortress/wikipedia-content-extractor`) Actor

Extract structured public Wikipedia content, page summaries, infobox-style fields, categories, and links for knowledge bases, research workflows, and enrichment pipelines. Pay-per-result.

- **URL**: https://apify.com/unrivaled\_fortress/wikipedia-content-extractor.md
- **Developed by:** [David Ahn](https://apify.com/unrivaled_fortress) (community)
- **Categories:** Education, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 results

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/platform/actors/running/actors-in-store#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

## 📖 Wikipedia Content Extractor

**Search Wikipedia and get clean, structured article summaries — instantly.**

Powered by the official MediaWiki API. No login, no API key, no blocks.

### ✨ Features

- 🔍 **Topic search** — search any topic (e.g. `black holes`, `K-pop`, `machine learning`)
- 📄 **Intro extracts** — get each article's clean text summary (no markup)
- 📊 **Rich metadata** — page ID, word count, watchers, last modified
- 🔗 **Direct links** — full URLs to every article
- ⚡ **Fast** — official API, results in seconds

### 💡 Use Cases

- 🧠 **Students** — quick research on any topic
- ✍️ **Writers** — gather source material and references
- 🤖 **AI/LLM training** — clean text corpus for model data
- 📰 **Journalists** — fact-check and background info
- 🔎 **Curious minds** — explore any subject systematically

### 📊 Output Fields

Each article includes:

- `title` — article title
- `pageId` / `url` — Wikipedia page ID and link
- `snippet` — search result snippet
- `extract` — clean intro text (plain text, no markup)
- `wordCount` / `size` — article statistics
- `watchers` — number of users watching the page
- `lastModified` — last edit timestamp

### 🚀 How It Works

1. Searches Wikipedia via the MediaWiki API
2. Fetches each result's intro extract
3. Returns clean, structured JSON

### 💰 Pricing

**from $2.00 / 1,000 results** — pay only for data returned.

### ⚙️ Technical

- Runtime: Python 3.11
- Data source: official MediaWiki API
- No API key required
- Execution time: ~3 seconds

***

**Wikipedia, structured and clean. Just enter a topic and run.**

# Actor input Schema

## `query` (type: `string`):

Topic to search on Wikipedia (e.g. 'black holes', 'K-pop')

## `limit` (type: `integer`):

Maximum number of articles to return (1-50)

## Actor input object example

```json
{
  "query": "artificial intelligence",
  "limit": 10
}
```

# 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 = {
    "query": "artificial intelligence"
};

// Run the Actor and wait for it to finish
const run = await client.actor("unrivaled_fortress/wikipedia-content-extractor").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 = { "query": "artificial intelligence" }

# Run the Actor and wait for it to finish
run = client.actor("unrivaled_fortress/wikipedia-content-extractor").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 '{
  "query": "artificial intelligence"
}' |
apify call unrivaled_fortress/wikipedia-content-extractor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=unrivaled_fortress/wikipedia-content-extractor",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/y8a7xCZB0xx248g3B/builds/SdCqWcJyHrQ9YU9xM/openapi.json