# Dailycity Discoveries Spider (`getdataforme/dailycity-discoveries-spider`) Actor The Dailycity Discoveries Spider is an Apify Actor that scrapes event-related data from the Daly City website, extracting web pages, links, and documents on topics like aquatics. It provides structured JSON output with customizable searches, high reliability, and fast processing.... - **URL**: https://apify.com/getdataforme/dailycity-discoveries-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 ### Description The Dailycity Discoveries Spider is an Apify Actor that scrapes event-related data from the Daly City website, extracting web pages, links, and documents on topics like aquatics. It provides structured JSON output with customizable searches, high reliability, and fast processing.... --- ### PART 1: README.md ## Dailycity Discoveries Spider The Dailycity Discoveries Spider is a powerful Apify Actor designed to scrape and extract event-related data from the Daly City website. It efficiently gathers information on various topics like aquatics, allowing users to discover upcoming events, documents, and web pages with ease. This tool is ideal for anyone needing structured, reliable data from local government resources without manual browsing. ### Features - **Comprehensive Data Extraction**: Scrapes web pages, links, and documents related to specified search topics from the Daly City website. - **Structured Output**: Provides clean JSON data with fields like event topics, dates, briefs, and URLs for easy integration. - **High Reliability**: Built with robust error handling to ensure consistent performance across runs. - **Customizable Searches**: Allows users to input specific search terms to target relevant content. - **Fast Processing**: Optimized for quick scraping, delivering results in minutes for most queries. - **Scalable**: Handles multiple result types, including web pages, links, and documents, with pagination support. - **Apify Integration**: Seamlessly runs on the Apify platform with built-in monitoring and export options. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | Search | string | No | The search term to query for events and related content on the Daly City website. Defaults to a general topic if not specified. | "aquatics" | ### Example Usage To run the actor, configure the input as follows: ```json { "Search": "aquatics" } ```` Example output: ```json [ { "result_type": "WebPages", "event_topic": "Aquatics", "url": "https://dalycity.org/330/aquatics", "event_date": "Jul 2, 2025", "event_brief": "For assistance with reservations and registration, contact our Administration Office at\u00a0(650) 991-8001. Aquatic Programs & LessonsAquatic Programs such as Lap Swim, Recreation Swim, Aqua Fit, and Swim Lessons will be offered. at Giammona Pool.", "document_size": null, "total_results": 69, "actor_id": "wyxH3R83Whs0Izq0a", "run_id": "X87bLpxIQkgelCBdL" }, { "result_type": "Links", "event_topic": "Aquatics", "url": "https://dalycity.org/330/Aquatics", "event_date": "Oct 2, 2020", "event_brief": "Our Aquatics area offers a variety of programs and activities.", "document_size": null, "total_results": 69, "actor_id": "wyxH3R83Whs0Izq0a", "run_id": "X87bLpxIQkgelCBdL" }, { "result_type": "Documents", "event_topic": "Graphic Buttons Aquatics 2", "url": "https://dalycity.org/documentcenter/view/3177", "event_date": "Oct 28, 2020", "event_brief": "1 KB", "document_size": "1 KB", "total_results": 69, "actor_id": "wyxH3R83Whs0Izq0a", "run_id": "X87bLpxIQkgelCBdL" } ] ``` ### Use Cases - **Market Research**: Analyze local event trends in Daly City for business insights. - **Competitive Intelligence**: Monitor community programs and activities for strategic planning. - **Price Monitoring**: Track costs related to aquatic programs or similar services. - **Content Aggregation**: Collect and organize event data for newsletters or websites. - **Academic Research**: Gather historical and current data on city events for studies. - **Business Automation**: Automate data collection for reports on municipal offerings. ### Installation and Usage 1. Search for "Dailycity Discoveries Spider" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 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 actor outputs a JSON array of objects, each representing a scraped item. Key fields include: - `result_type`: Type of result (e.g., "WebPages", "Links", "Documents"). - `event_topic`: The topic or title of the event/content. - `url`: Direct link to the page or document. - `event_date`: Date associated with the event (if available). - `event_brief`: A short description or summary. - `document_size`: Size of the document (null for non-documents). - `total_results`: Total number of results found. - `actor_id` and `run_id`: Metadata for tracking the Apify run. This structure ensures easy parsing and integration into downstream applications. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** ### PART 2: Generate Description # Actor input Schema ## `Search` (type: `string`): The search for the spider. ## Actor input object example ```json { "Search": "aquatics" } ``` # 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/dailycity-discoveries-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/dailycity-discoveries-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/dailycity-discoveries-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/dailycity-discoveries-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dailycity Discoveries Spider", "description": "The Dailycity Discoveries Spider is an Apify Actor that scrapes event-related data from the Daly City website, extracting web pages, links, and documents on topics like aquatics. It provides structured JSON output with customizable searches, high reliability, and fast processing....", "version": "0.0", "x-build-id": "ds7GsZ1e2XEJ6Ldkm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~dailycity-discoveries-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-dailycity-discoveries-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~dailycity-discoveries-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-dailycity-discoveries-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~dailycity-discoveries-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-dailycity-discoveries-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Search": { "title": "Search", "type": "string", "description": "The search for the spider.", "default": "aquatics" } } }, "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 } } } } } } } } } } ```