# TheMealDB Recipes Scraper (`ninhothedev/themealdb-scraper`) Actor $0.5/1K 🔥 Fast TheMealDB recipes scraper! Meals by name, category, area or ingredient — full ingredients & instructions. No key. JSON, CSV, Excel or API in seconds. Pull thousands for recipe apps & food datasets ⚡ - **URL**: https://apify.com/ninhothedev/themealdb-scraper.md - **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community) - **Categories:** Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Recipes Scraper 🍽️ Scrape structured **recipe data** from [TheMealDB](https://www.themealdb.com/) — meals with full **ingredients + measures**, step-by-step **instructions**, **categories**, **cuisines (areas)**, tags, thumbnail images and YouTube links. Search by meal name or filter by category, area or main ingredient. **No API key required.** Perfect for building recipe apps, food datasets, meal-planning tools and content pipelines. --- ### ✨ What it does - **Search by name** — pass queries like `chicken`, `pasta`, `curry` and get full meals back. - **Filter by category / area / ingredient** — e.g. all `Seafood` meals, all `Italian` dishes, or everything made with `chicken_breast`. - **Full enrichment** — filtered results are slim by default on TheMealDB; enable `enrichFull` to fetch complete ingredients, measures and instructions for every match. - **Clean, normalized output** — the messy `strIngredient1..20` / `strMeasure1..20` fields are paired into a single tidy `ingredients` list and tags are split into an array. ### 📤 Output One dataset item per meal: | Field | Description | |-------|-------------| | `id` | TheMealDB meal id | | `name` | Meal name | | `category` | Category (e.g. Seafood, Dessert) | | `area` | Cuisine / area (e.g. Italian, Indian) | | `instructions` | Cooking steps (capped at 6000 chars) | | `thumbnail` | Meal image URL | | `tags` | List of tags | | `youtube` | YouTube tutorial link | | `source_url` | Original recipe source | | `ingredients` | List of `"measure ingredient"` strings | | `source` | Always `themealdb` | | `scraped_at` | ISO timestamp | ```json { "id": "52795", "name": "Chicken Handi", "category": "Chicken", "area": "Indian", "instructions": "Take a large pot or wok...", "thumbnail": "https://www.themealdb.com/images/media/meals/wyxwsp1486979827.jpg", "tags": [], "youtube": "https://www.youtube.com/watch?v=IO0issT0Rmc", "source_url": null, "ingredients": ["1.2 kg Chicken", "5 thinly sliced Onion", "..."], "source": "themealdb", "scraped_at": "2026-07-20T12:00:00+00:00" } ```` ### ⚙️ Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `mode` | select | `search` | `search` (by name) or `filter` (by category/area/ingredient) | | `queries` | array | `["chicken","pasta"]` | Meal name queries (mode=search) | | `filterType` | select | `category` | `category`, `area` or `ingredient` (mode=filter) | | `filterValue` | string | – | Value to filter by, e.g. `Seafood` | | `enrichFull` | boolean | `true` | Look up full details for filtered results | | `maxItems` | integer | `100` | Max meals to output (max 1000) | #### Example — search ```json { "mode": "search", "queries": ["chicken", "curry"], "maxItems": 50 } ``` #### Example — filter by category with full data ```json { "mode": "filter", "filterType": "category", "filterValue": "Seafood", "enrichFull": true } ``` ### 💰 Pricing Pay-per-result, roughly **$0.5 per 1,000 meals**. TheMealDB is datacenter-friendly, so runs are fast and cheap — no residential proxies needed. ### 🎯 Use cases - **Recipe apps** — seed your app with structured, ready-to-use recipes. - **Food datasets** — build ML / analytics datasets of meals, ingredients and cuisines. - **Meal planning** — power weekly planners and shopping-list generators. - **Content** — food blogs, newsletters and social pipelines. ### 🔗 Related actors - [Recipe Scraper](https://apify.com/ninhothedev/recipe-scraper) - [Cocktail Scraper](https://apify.com/ninhothedev/cocktail-scraper) - [Open Food Facts Scraper](https://apify.com/ninhothedev/open-food-facts-scraper) - [UPC Barcode Lookup Scraper](https://apify.com/ninhothedev/upc-barcode-lookup-scraper) ### 🏷️ Keywords themealdb, recipe scraper, meal data, food api, ingredients, cooking, recipe api, meal dataset, cuisine, food scraper *** *Data sourced from the public TheMealDB API. Use responsibly and in line with TheMealDB's terms.* # Actor input Schema ## `mode` (type: `string`): How to fetch meals. 'search' looks up full meals by name query. 'filter' lists meals by category, area or ingredient. ## `queries` (type: `array`): Meal name queries used when mode='search' (e.g. 'chicken', 'pasta'). Each returns full meals with ingredients and instructions. ## `filterType` (type: `string`): Which dimension to filter by when mode='filter'. ## `filterValue` (type: `string`): Value to filter by when mode='filter' (e.g. 'Seafood' for category, 'Italian' for area, 'chicken\_breast' for ingredient). ## `enrichFull` (type: `boolean`): When mode='filter', look up each meal id to fetch full details (ingredients, instructions, tags). If false, only slim data (id, name, thumbnail) is returned. ## `maxItems` (type: `integer`): Maximum number of meals to output across all queries/filters. ## Actor input object example ```json { "mode": "search", "queries": [ "chicken", "pasta" ], "filterType": "category", "filterValue": "Seafood", "enrichFull": true, "maxItems": 100 } ``` # 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 = { "queries": [ "chicken", "pasta" ] }; // Run the Actor and wait for it to finish const run = await client.actor("ninhothedev/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 = { "queries": [ "chicken", "pasta", ] } # Run the Actor and wait for it to finish run = client.actor("ninhothedev/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 '{ "queries": [ "chicken", "pasta" ] }' | apify call ninhothedev/themealdb-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ninhothedev/themealdb-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TheMealDB Recipes Scraper", "description": "$0.5/1K 🔥 Fast TheMealDB recipes scraper! Meals by name, category, area or ingredient — full ingredients & instructions. No key. JSON, CSV, Excel or API in seconds. Pull thousands for recipe apps & food datasets ⚡", "version": "0.1", "x-build-id": "ifW8WTXiOVNfOupr1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ninhothedev~themealdb-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ninhothedev-themealdb-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/ninhothedev~themealdb-scraper/runs": { "post": { "operationId": "runs-sync-ninhothedev-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/ninhothedev~themealdb-scraper/run-sync": { "post": { "operationId": "run-sync-ninhothedev-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": { "mode": { "title": "Mode", "enum": [ "search", "filter" ], "type": "string", "description": "How to fetch meals. 'search' looks up full meals by name query. 'filter' lists meals by category, area or ingredient.", "default": "search" }, "queries": { "title": "Search queries", "type": "array", "description": "Meal name queries used when mode='search' (e.g. 'chicken', 'pasta'). Each returns full meals with ingredients and instructions.", "items": { "type": "string" } }, "filterType": { "title": "Filter type", "enum": [ "category", "area", "ingredient" ], "type": "string", "description": "Which dimension to filter by when mode='filter'.", "default": "category" }, "filterValue": { "title": "Filter value", "type": "string", "description": "Value to filter by when mode='filter' (e.g. 'Seafood' for category, 'Italian' for area, 'chicken_breast' for ingredient)." }, "enrichFull": { "title": "Enrich filtered results with full data", "type": "boolean", "description": "When mode='filter', look up each meal id to fetch full details (ingredients, instructions, tags). If false, only slim data (id, name, thumbnail) is returned.", "default": true }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of meals to output across all queries/filters.", "default": 100 } } }, "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 } } } } } } } } } } ```