# Podchaser Discovery Spider (`getdataforme/podchaser-discovery-spider`) Actor The Podchaser Discovery Spider is a tool for scraping comprehensive podcast data from Podchaser, enabling efficient discovery and analysis.... - **URL**: https://apify.com/getdataforme/podchaser-discovery-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Lead generation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 --- ## README.md ### Podchaser Discovery Spider #### Introduction The Podchaser Discovery Spider is a powerful tool designed to scrape and gather comprehensive data from the Podchaser platform. It enables users to efficiently discover podcasts based on specific queries, providing detailed insights into podcast content, ratings, and metadata. #### Features - **Comprehensive Data Extraction**: Scrape detailed information about podcasts including titles, descriptions, images, URLs, episode counts, and more. - **Customizable Queries**: Tailor searches with user-defined queries to find relevant podcasts. - **Flexible Item Limiting**: Set a maximum number of items to scrape per run for efficient data management. - **High-Quality Data Output**: Ensures reliable and accurate podcast information is captured. - **User-Friendly Configuration**: Easy setup with intuitive input parameters. #### Input Parameters Table | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------------------------|----------------| | Queries | array | No | The queries for the spider. | `["english"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | #### Example Usage ##### Input JSON ```json { "Queries": ["english"], "item_limit": 10 } ```` ##### Output JSON ```json [ { "query": "english", "podcast_id": 4947934, "title": "Business English from All Ears English", "description": "Want to feel more confident with your business English? Lindsay and Michelle show you real, business English vocabulary for the global economy in this podcast from All Ears English. Learn spoken English for business with native speakers...", "image_url": "https://megaphone.imgix.net/podcasts/9b344c08-1f4b-11ed-8dbe-bfd7cf20a20f/image/AEE_Show_Cover_Art_Business_English-Less-than-500.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format%2Ccompress", "podcast_url": "https://feeds.megaphone.fm/ALLE3395867426", "episode_count": 605, "rating": 5, "itunes_id": "1650591741", "spotify_id": "5HFUjaqFFXUiHC5SA6uVEz", "date_of_first_episode": "2022-10-17 00:15:00", "date_of_latest_episode": "2026-05-28 05:00:00", "itunes_author": "Lindsay McMahon", "networks": [], "categories": ["Business", "Education"], "follower_count": 16, "review_count": 0, "rating_count": 1, "actor_id": "72Hnz7JLL2DF7caBe", "run_id": "W5AQLuamigrjnSxgG" }, { "query": "english", "podcast_id": 509053, "title": "Speak English Now Podcast: Learn English | Speak English without grammar.", "description": "The Speak English Now Podcast is your resource for practicing your English speaking and listening. You will learn English with the Question and Answer (TPRS) and Point of View techniques...", "image_url": "https://static.libsyn.com/p/assets/a/5/f/d/a5fd64b28945d2ce16c3140a3186d450/Speak-English-Now-Artwork_2026-17001.jpg", "podcast_url": "https://speakenglishpodcast.libsyn.com/rss", "episode_count": 400, "rating": 5, "itunes_id": "1216879060", "spotify_id": "3BdVI2jDY4FRY4QKKbY1GM", "date_of_first_episode": "2017-03-16 09:41:57", "date_of_latest_episode": "2026-05-14 13:00:00", "itunes_author": "Georgiana, founder of SpeakEnglishPodcast.com", "networks": [], "categories": ["Education"], "follower_count": 60, "review_count": 1, "rating_count": 3, "actor_id": "72Hnz7JLL2DF7caBe", "run_id": "W5AQLuamigrjnSxgG" } ] ``` #### Use Cases - **Market Research and Analysis**: Discover trending podcasts to understand market dynamics. - **Competitive Intelligence**: Analyze competitor podcast offerings for strategic insights. - **Price Monitoring**: Track changes in podcast-related services or products. - **Content Aggregation**: Compile a list of relevant podcasts for content curation. - **Academic Research**: Gather data for studies on media consumption and trends. - **Business Automation**: Automate the discovery process for marketing strategies. #### Installation and Usage 1. Search for "Podchaser Discovery Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). #### Output Format The output is a JSON array containing objects with fields such as `query`, `podcast_id`, `title`, `description`, `image_url`, `podcast_url`, `episode_count`, `rating`, `itunes_id`, `spotify_id`, `date_of_first_episode`, `date_of_latest_episode`, `itunes_author`, `networks`, `categories`, `follower_count`, `review_count`, `rating_count`, `actor_id`, and `run_id`. Each field provides specific details about the podcasts discovered. #### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Get Data For Me](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! *** # Actor input Schema ## `Queries` (type: `array`): The queries for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "Queries": [ "english" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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("getdataforme/podchaser-discovery-spider").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("getdataforme/podchaser-discovery-spider").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 getdataforme/podchaser-discovery-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/podchaser-discovery-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Podchaser Discovery Spider", "description": "The Podchaser Discovery Spider is a tool for scraping comprehensive podcast data from Podchaser, enabling efficient discovery and analysis....", "version": "0.0", "x-build-id": "iQNEIZrYwsTgBZCqg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~podchaser-discovery-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-podchaser-discovery-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~podchaser-discovery-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-podchaser-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~podchaser-discovery-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-podchaser-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Queries": { "title": "Queries", "type": "array", "description": "The queries for the spider.", "default": [ "english" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```