# TheMealDB Recipe Scraper (`lulzasaur/themealdb-scraper`) Actor Scrape meal recipes from TheMealDB. Search by name, filter by category (Beef, Chicken, Seafood, etc.) or cuisine (Italian, Mexican, Indian, etc.). Get ingredients, instructions, images, and YouTube links. - **URL**: https://apify.com/lulzasaur/themealdb-scraper.md - **Developed by:** [lulz bot](https://apify.com/lulzasaur) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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 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 ## TheMealDB Recipe Scraper Scrape meal recipes from [TheMealDB](https://www.themealdb.com/) — a free, open database of recipes from around the world. ### What does TheMealDB Scraper do? This scraper extracts detailed recipe information from TheMealDB, including: - **Meal name and ID** - **Category** (Beef, Chicken, Dessert, Seafood, Pasta, Vegan, etc.) - **Cuisine/Area** (Italian, Mexican, Indian, Japanese, French, etc.) - **Full cooking instructions** - **Ingredients list** with measurements (parsed into clean arrays) - **Image URL** for the meal - **Tags** (e.g. Meat, Casserole, Curry) - **YouTube video link** for cooking tutorials - **Source URL** for the original recipe ### How to search You can search in three ways: #### 1. Search by meal name Enter one or more search queries like "chicken", "pasta", "cake". The scraper will find all meals matching each query. #### 2. Filter by category Select a food category to get all meals in that group: Beef, Chicken, Dessert, Lamb, Miscellaneous, Pasta, Pork, Seafood, Side, Starter, Vegan, Vegetarian, Breakfast, Goat #### 3. Filter by cuisine/area Select a cuisine to get all meals from that region: American, Italian, Mexican, Indian, Japanese, French, Chinese, Thai, British, Greek, and 27 more. ### Input example ```json { "searchQueries": ["pasta", "risotto"], "maxListings": 20 } ```` Or filter by category: ```json { "category": "Seafood", "maxListings": 30 } ``` Or filter by cuisine: ```json { "area": "Italian", "maxListings": 25 } ``` ### Output example ```json { "mealName": "Teriyaki Chicken Casserole", "mealId": "52772", "category": "Chicken", "area": "Japanese", "instructions": "Preheat oven to 350 F. Spray a 9x13-inch baking pan...", "ingredients": [ { "ingredient": "soy sauce", "measure": "3/4 cup" }, { "ingredient": "water", "measure": "1/2 cup" }, { "ingredient": "brown sugar", "measure": "1/4 cup" }, { "ingredient": "ground ginger", "measure": "1/2 teaspoon" }, { "ingredient": "chicken breasts", "measure": "2" } ], "imageUrl": "https://www.themealdb.com/images/media/meals/wvpsxx1468256321.jpg", "tags": ["Meat", "Casserole"], "youtubeUrl": "https://www.youtube.com/watch?v=4aZr5hZXP_s", "sourceUrl": null, "scrapedAt": "2026-04-26T12:00:00.000Z" } ``` ### Use cases - **Recipe apps** — Build a meal planning or recipe search app - **Meal prep tools** — Aggregate recipes by category or cuisine for weekly meal plans - **Nutrition research** — Analyze ingredients across different cuisines - **Food blogs** — Source recipe data for content creation - **Grocery list generators** — Extract ingredient lists to build shopping lists - **Cooking education** — Collect recipes with video tutorials for learning platforms ### Cost This scraper uses pay-per-event pricing. You are charged $0.005 per recipe result returned. There are no platform fees beyond the per-result charge. | Results | Cost | |---------|------| | 50 | $0.25 | | 200 | $1.00 | | 500 | $2.50 | ### Limitations - TheMealDB's free tier API (key "1") is used. Data is sourced from the TheMealDB community database. - The database contains a curated set of recipes (300+). Not all possible meals are available. - Search is by meal name substring match. Ingredient-based search is not supported in this scraper. ### Integrations Connect TheMealDB Scraper to any workflow with [Apify integrations](https://apify.com/integrations). Export to Google Sheets, Slack, Zapier, Make, GitHub, and more. # Actor input Schema ## `searchQueries` (type: `array`): Search meals by name. Each query searches for matching meal names (e.g. 'chicken', 'pasta', 'cake'). ## `category` (type: `string`): Filter meals by category. Leave empty to use search queries instead. ## `area` (type: `string`): Filter meals by cuisine/area. Leave empty to use search queries instead. ## `maxListings` (type: `integer`): Maximum number of meal recipes to return. ## `proxyConfiguration` (type: `object`): Optional proxy to use for API requests. ## Actor input object example ```json { "searchQueries": [ "chicken" ], "category": "", "area": "", "maxListings": 50 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("lulzasaur/themealdb-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("lulzasaur/themealdb-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 '{}' | apify call lulzasaur/themealdb-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lulzasaur/themealdb-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TheMealDB Recipe Scraper", "description": "Scrape meal recipes from TheMealDB. Search by name, filter by category (Beef, Chicken, Seafood, etc.) or cuisine (Italian, Mexican, Indian, etc.). Get ingredients, instructions, images, and YouTube links.", "version": "1.0", "x-build-id": "s9q0bEkyVMkReJ5Ra" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lulzasaur~themealdb-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lulzasaur-themealdb-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lulzasaur~themealdb-scraper/runs": { "post": { "operationId": "runs-sync-lulzasaur-themealdb-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lulzasaur~themealdb-scraper/run-sync": { "post": { "operationId": "run-sync-lulzasaur-themealdb-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "Search meals by name. Each query searches for matching meal names (e.g. 'chicken', 'pasta', 'cake').", "items": { "type": "string" }, "default": [ "chicken" ] }, "category": { "title": "Category", "enum": [ "", "Beef", "Chicken", "Dessert", "Lamb", "Miscellaneous", "Pasta", "Pork", "Seafood", "Side", "Starter", "Vegan", "Vegetarian", "Breakfast", "Goat" ], "type": "string", "description": "Filter meals by category. Leave empty to use search queries instead.", "default": "" }, "area": { "title": "Cuisine / Area", "enum": [ "", "Algerian", "American", "Argentinian", "Australian", "British", "Canadian", "Chinese", "Croatian", "Dutch", "Egyptian", "Filipino", "French", "Greek", "Indian", "Irish", "Italian", "Jamaican", "Japanese", "Kenyan", "Malaysian", "Mexican", "Moroccan", "Norwegian", "Polish", "Portuguese", "Russian", "Saudi Arabian", "Slovakian", "Spanish", "Syrian", "Thai", "Tunisian", "Turkish", "Ukrainian", "Uruguayan", "Venezulan", "Vietnamese" ], "type": "string", "description": "Filter meals by cuisine/area. Leave empty to use search queries instead.", "default": "" }, "maxListings": { "title": "Max Listings", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of meal recipes to return.", "default": 50 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional proxy to use for API requests." } } }, "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 } } } } } } } } } } ```