# Forevermissed Discovery Parser Spider (`getdataforme/forevermissed-discovery-parser-spider`) Actor The Forevermissed Discovery Parser Spider is a web scraping tool for extracting memorial information from ForeverMissed. It offers keyword-based searches, high data quality, and flexible item limits.... - **URL**: https://apify.com/getdataforme/forevermissed-discovery-parser-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **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 --- ## Forevermissed Discovery Parser Spider ### Introduction The **Forevermissed Discovery Parser Spider** is a powerful web scraping tool designed to extract detailed memorial information from the ForeverMissed website. It allows users to efficiently gather data based on specific keywords, providing valuable insights into memorialized individuals and their commemorations. ### Features - **Keyword-Based Scraping**: Customize searches with specific keywords to target relevant memorials. - **High Data Quality**: Ensures accurate and reliable extraction of memorial details. - **Flexible Item Limit**: Control the number of items scraped per run for efficient data management. - **Comprehensive Output**: Captures essential fields such as full name, image URL, and description. - **User-Friendly Configuration**: Simple setup with intuitive input parameters. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|----------|----------|-----------------------------------------------------------------------------|--------------------------| | Keywords | array | No | The keywords for the spider. | `["Love", "help"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Keywords": ["Love", "help"], "item_limit": 5 } ```` #### Output JSON ```json [ { "keyword": "help", "full_name": "Kenneth Wade Phelps", "image_url": "https://www.forevermissed.com/assets/white-label/forevermissed/images/no_img.png?rel=1779788725322", "about_link": "https://www.forevermissed.com/kenneth-wade-phelpsjr/about", "creator_name": "Joyce Stansbury", "created_date": "March 20, 2026", "description": "This memorial website was created in memory of our loved one, Kenneth Phelps. We will remember him forever....", "actor_id": "PLJypnoVwziuyhcmV", "run_id": "W9YIa6X1twleoNTbC" }, { "keyword": "help", "full_name": "Kevin Wayne Phelps", "image_url": "https://photo.forevermissed.com/lst/k/e/kevin-wayne-phelps/p/4507868_600x400_bb01b1.jpg", "about_link": "https://www.forevermissed.com/kevin-wayne-phelps/about", "creator_name": "Tony Baker", "created_date": "June 6, 2025", "description": "This memorial website was created in memory of our loved one, Kevin Phelps. We will remember him forever....", "actor_id": "PLJypnoVwziuyhcmV", "run_id": "W9YIa6X1twleoNTbC" }, { "keyword": "help", "full_name": "ellen ann phelps", "image_url": "https://www.forevermissed.com/assets/white-label/forevermissed/images/no_img.png?rel=1779788725322", "about_link": "https://www.forevermissed.com/ellen-ann-phelps/about", "creator_name": "Wyndi Doss", "created_date": "February 27, 2025", "description": "This memorial website was created in memory of our loved one, ellen phelps. We will remember her forever....", "actor_id": "PLJypnoVwziuyhcmV", "run_id": "W9YIa6X1twleoNTbC" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data for demographic studies or market insights. - **Competitive Intelligence**: Monitor competitor activities related to memorial services. - **Price Monitoring**: Track pricing trends in memorial products or services. - **Content Aggregation**: Compile comprehensive datasets for analysis or reporting. - **Academic Research**: Support research projects focused on social and cultural studies. - **Business Automation**: Automate data collection processes for efficiency. ### Installation and Usage 1. Search for "Forevermissed Discovery Parser 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 where each object contains: - `keyword`: The keyword used for scraping. - `full_name`: Full name of the memorialized individual. - `image_url`: URL to the image associated with the memorial. - `about_link`: Link to the detailed about page on ForeverMissed. - `creator_name`: Name of the person who created the memorial. - `created_date`: Date when the memorial was created. - `description`: Brief description of the memorial. - `actor_id`: Unique identifier for the actor run. - `run_id`: Unique identifier for the specific run. ### 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 ## `Keywords` (type: `array`): The keywords 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 { "Keywords": [ "Love", "help", "trust" ], "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/forevermissed-discovery-parser-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/forevermissed-discovery-parser-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/forevermissed-discovery-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/forevermissed-discovery-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Forevermissed Discovery Parser Spider", "description": "The Forevermissed Discovery Parser Spider is a web scraping tool for extracting memorial information from ForeverMissed. It offers keyword-based searches, high data quality, and flexible item limits....", "version": "0.0", "x-build-id": "sKx4bQGfdJheUYgsa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~forevermissed-discovery-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-forevermissed-discovery-parser-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~forevermissed-discovery-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-forevermissed-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~forevermissed-discovery-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-forevermissed-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Keywords": { "title": "Keywords", "type": "array", "description": "The keywords for the spider.", "default": [ "Love", "help", "trust" ], "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 } } } } } } } } } } ```