# Wikipedia Articles Scraper (`scrupulous_waterbird_m4w/wikipedia-articles`) Actor Pull Wikipedia articles by title, free-text search, or category. Returns summary, full article HTML with sections, search results, or category members via the public MediaWiki REST + Action API (no auth, no proxy). - **URL**: https://apify.com/scrupulous\_waterbird\_m4w/wikipedia-articles.md - **Developed by:** [Mori](https://apify.com/scrupulous_waterbird_m4w) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Articles Scraper Pull Wikipedia articles by title or category. Returns article body, summary, links, categories, infobox data via the public MediaWiki REST API. No auth, no proxy.. ### What it does - Extracts ITEM_TYPE from URL_LIST - Outputs structured JSON (SCHEMA_LINK) - Handles pagination, retries, proxy rotation out of the box ### Input | Field | Required | Description | |---|---|---| | `startUrls` | yes | List of URLs to scrape | | `maxRequestsPerRun` | no | Cap on total requests (default 100) | | `proxyConfiguration` | no | Proxy settings (default: Apify Proxy) | ### Output One record per source page: ```json { "url": "...", "title": "...", "items": [...], "scrapedAt": "ISO-8601" } ```` ### Pricing Free tier: 100 runs/month. Pro: $X/mo. ### Limitations - Static HTML only (no JS rendering — use the playwright template for SPAs). - Single-domain by default. Set `enqueueLinks` globs to crawl. ### Changelog - 0.1.0 — initial release. # Actor input Schema ## `titles` (type: `string`): Pipe-separated list of article titles for modes `summary` or `full` (e.g. 'Albert Einstein|Marie Curie|Ada Lovelace'). For mode `search`, a free-text search query. For mode `category`, a category name without the 'Category:' prefix (e.g. 'Physicists'). ## `mode` (type: `string`): What to fetch. One of: 'summary' (default — REST /page/summary for each pipe-separated title), 'full' (REST /page/html + mobile-sections for each title), 'search' (Action API list=search with titles as the free-text query, then summary for each top hit), 'category' (Action API list=categorymembers with titles as the category name, then full HTML for each top hit). ## `maxItems` (type: `integer`): Number of articles to fetch per run. For `summary`/`full` this caps how many pipe-separated titles are processed. For `search`/`category` this caps how many top results are fetched. ## Actor input object example ```json { "titles": "Albert Einstein|Marie Curie|Ada Lovelace", "mode": "summary", "maxItems": 5 } ``` # 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 = { "titles": "Albert Einstein|Marie Curie|Ada Lovelace" }; // Run the Actor and wait for it to finish const run = await client.actor("scrupulous_waterbird_m4w/wikipedia-articles").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 = { "titles": "Albert Einstein|Marie Curie|Ada Lovelace" } # Run the Actor and wait for it to finish run = client.actor("scrupulous_waterbird_m4w/wikipedia-articles").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 '{ "titles": "Albert Einstein|Marie Curie|Ada Lovelace" }' | apify call scrupulous_waterbird_m4w/wikipedia-articles --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrupulous_waterbird_m4w/wikipedia-articles", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Articles Scraper", "description": "Pull Wikipedia articles by title, free-text search, or category. Returns summary, full article HTML with sections, search results, or category members via the public MediaWiki REST + Action API (no auth, no proxy).", "version": "0.0", "x-build-id": "4rVBSoomiJ7dK55VX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrupulous_waterbird_m4w~wikipedia-articles/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrupulous_waterbird_m4w-wikipedia-articles", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrupulous_waterbird_m4w~wikipedia-articles/runs": { "post": { "operationId": "runs-sync-scrupulous_waterbird_m4w-wikipedia-articles", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrupulous_waterbird_m4w~wikipedia-articles/run-sync": { "post": { "operationId": "run-sync-scrupulous_waterbird_m4w-wikipedia-articles", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "titles", "mode" ], "properties": { "titles": { "title": "Titles / query / category", "type": "string", "description": "Pipe-separated list of article titles for modes `summary` or `full` (e.g. 'Albert Einstein|Marie Curie|Ada Lovelace'). For mode `search`, a free-text search query. For mode `category`, a category name without the 'Category:' prefix (e.g. 'Physicists')." }, "mode": { "title": "Mode", "type": "string", "description": "What to fetch. One of: 'summary' (default — REST /page/summary for each pipe-separated title), 'full' (REST /page/html + mobile-sections for each title), 'search' (Action API list=search with titles as the free-text query, then summary for each top hit), 'category' (Action API list=categorymembers with titles as the category name, then full HTML for each top hit).", "default": "summary" }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 50, "type": "integer", "description": "Number of articles to fetch per run. For `summary`/`full` this caps how many pipe-separated titles are processed. For `search`/`category` this caps how many top results are fetched.", "default": 5 } } }, "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 } } } } } } } } } } ```