# Wikipedia Scraper (`dami_studio/wikipedia-scraper`) Actor Search Wikipedia by keyword or fetch clean structured page data (plain-text extract, thumbnail, categories, URL) for given titles, via the public MediaWiki API. No key, no anti-bot. Search paginates; titles batch 50 at a time. - **URL**: https://apify.com/dami\_studio/wikipedia-scraper.md - **Developed by:** [Dami's Studio](https://apify.com/dami_studio) (community) - **Categories:** Integrations, AI, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 page returneds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 Scraper Search Wikipedia by keyword, or fetch clean, structured page data for exact titles — straight from the official **MediaWiki Action API**. No API key, no login, no anti-bot. ### Two modes **1. Search** — set `searchQuery`. Returns matching articles with `title`, `pageid`, `url`, a plain-text `snippet` (the API's HTML is stripped for you), `wordcount`, `size`, and `timestamp`. The actor paginates automatically (50 per request) up to `maxItems`. **2. Page data** — set `pageTitles` (a list of exact article titles). Returns `title`, `pageid`, `url`, the plain-text `extract`, a `thumbnail` image URL, and `categories`. Titles are batched 50 at a time. Turn on `fullText` to get the **whole article** instead of just the intro. (If both are provided, search mode wins. Provide one or the other.) ### What you get per row | Field | Mode | Notes | |---|---|---| | `title` | both | Article title. | | `pageid` | both | Stable Wikipedia page id (used to dedupe). | | `url` | both | Canonical article URL. | | `snippet` | search | Plain-text match snippet (HTML stripped). | | `wordcount`, `size`, `timestamp` | search | Article word count, byte size, last-edit time. | | `extract` | page | Plain-text article text (intro, or full body with `fullText`). | | `thumbnail` | page | Lead image URL (up to 400px), if the page has one. | | `categories` | page | Visible category names (hidden categories excluded). | ### Input | Field | Notes | |---|---| | `searchQuery` | Keywords, e.g. `machine learning`. Leave empty if using titles. | | `pageTitles` | List of exact titles, e.g. `["Apify", "Web scraping"]`. | | `fullText` | Page mode only. Full article text vs. just the intro. Default off. | | `language` | Wikipedia edition: `en`, `fr`, `de`, `es`, `ja`, … Default `en`. | | `maxItems` | Cap on returned pages. Default 50. | ### Output One dataset row per page (`ok: true`). Charged per `page`. Empty searches or unknown titles return a non-charged diagnostic row with an `errorCode` and a human-readable reason instead of silently returning nothing. ### Example ```json { "searchQuery": "machine learning", "language": "en", "maxItems": 30 } ```` ```json { "pageTitles": ["Apify", "Web scraping"], "fullText": false, "language": "en" } ``` ### Notes Uses `https://{language}.wikipedia.org/w/api.php`. Per Wikimedia's policy the actor always sends a descriptive `User-Agent` with a contact. Results are deduped by `pageid`. # Actor input Schema ## `searchQuery` (type: `string`): Keywords to search Wikipedia for (e.g. "machine learning"). Returns matching pages with snippet, word count, and URL. Leave empty if you instead provide exact Page titles below. ## `pageTitles` (type: `array`): Exact Wikipedia article titles to fetch full data for (plain-text extract, thumbnail, categories, URL). Batched 50 at a time. Use this OR a search query. ## `fullText` (type: `boolean`): Only applies in Page titles mode. When on, returns the whole article as plain text instead of just the intro paragraph(s). ## `language` (type: `string`): Wikipedia language edition code, e.g. en, fr, de, es, ja. Picks the host {lang}.wikipedia.org. ## `maxItems` (type: `integer`): Maximum number of pages to return. In search mode the actor paginates until it reaches this. In page-titles mode it caps how many titles are fetched. ## `notionConnector` (type: `string`): Optional. Write each page as a page into your Notion when the run finishes. Authorize a Notion connector once in Settings → API & Integrations → MCP connectors, then pick it here. Leave empty to skip (default) — results are always saved to the dataset regardless. ## `notionParentId` (type: `string`): Optional. The Notion data source ID of the database to write into (only used if a Notion connector is set). Leave empty to create the pages privately in your workspace instead. ## `proxyConfiguration` (type: `object`): Proxy used for the MediaWiki API requests. Apify Proxy is fine; Wikipedia has no anti-bot, so this is optional. ## Actor input object example ```json { "searchQuery": "machine learning", "pageTitles": [ "Apify", "Web scraping" ], "fullText": false, "language": "en", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `results` (type: `string`): Scraped rows are stored in the default dataset (one row per result). Blocked/empty/error runs return a single uncharged diagnostic row instead. # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchQuery": "machine learning", "pageTitles": [ "Apify", "Web scraping" ] }; // Run the Actor and wait for it to finish const run = await client.actor("dami_studio/wikipedia-scraper").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "searchQuery": "machine learning", "pageTitles": [ "Apify", "Web scraping", ], } # Run the Actor and wait for it to finish run = client.actor("dami_studio/wikipedia-scraper").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 '{ "searchQuery": "machine learning", "pageTitles": [ "Apify", "Web scraping" ] }' | apify call dami_studio/wikipedia-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dami_studio/wikipedia-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Scraper", "description": "Search Wikipedia by keyword or fetch clean structured page data (plain-text extract, thumbnail, categories, URL) for given titles, via the public MediaWiki API. No key, no anti-bot. Search paginates; titles batch 50 at a time.", "version": "0.1", "x-build-id": "pFcuHKocJAYHCuSbq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dami_studio~wikipedia-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dami_studio-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dami_studio~wikipedia-scraper/runs": { "post": { "operationId": "runs-sync-dami_studio-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dami_studio~wikipedia-scraper/run-sync": { "post": { "operationId": "run-sync-dami_studio-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "searchQuery": { "title": "Search query", "type": "string", "description": "Keywords to search Wikipedia for (e.g. \"machine learning\"). Returns matching pages with snippet, word count, and URL. Leave empty if you instead provide exact Page titles below." }, "pageTitles": { "title": "Page titles (instead of search)", "type": "array", "description": "Exact Wikipedia article titles to fetch full data for (plain-text extract, thumbnail, categories, URL). Batched 50 at a time. Use this OR a search query.", "default": [], "items": { "type": "string" } }, "fullText": { "title": "Fetch full article text", "type": "boolean", "description": "Only applies in Page titles mode. When on, returns the whole article as plain text instead of just the intro paragraph(s).", "default": false }, "language": { "title": "Wikipedia language", "type": "string", "description": "Wikipedia language edition code, e.g. en, fr, de, es, ja. Picks the host {lang}.wikipedia.org.", "default": "en" }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of pages to return. In search mode the actor paginates until it reaches this. In page-titles mode it caps how many titles are fetched.", "default": 50 }, "notionConnector": { "title": "Notion connector (optional)", "type": "string", "description": "Optional. Write each page as a page into your Notion when the run finishes. Authorize a Notion connector once in Settings → API & Integrations → MCP connectors, then pick it here. Leave empty to skip (default) — results are always saved to the dataset regardless." }, "notionParentId": { "title": "Notion target data source ID", "type": "string", "description": "Optional. The Notion data source ID of the database to write into (only used if a Notion connector is set). Leave empty to create the pages privately in your workspace instead." }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Proxy used for the MediaWiki API requests. Apify Proxy is fine; Wikipedia has no anti-bot, so this is optional.", "default": { "useApifyProxy": false } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```