# Gong Discovery Parser Spider (`getdataforme/gong-discovery-parser-spider`) Actor The Gong Discovery Parser Spider is a web scraping tool designed to extract data from Gongโ€™s platform based on specified keywords.... - **URL**: https://apify.com/getdataforme/gong-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 --- ## Gong Discovery Parser Spider ### Introduction The Gong Discovery Parser Spider is a powerful web scraping tool designed to extract valuable data from Gong's platform. It enables users to gather insights on webinars and articles by specifying keywords, making it an essential asset for market research, competitive intelligence, and content aggregation. ### Features - **Keyword-Based Scraping**: Customize your search with specific keywords to target relevant content. - **Flexible Item Limit**: Set a maximum number of items to scrape per run or opt for unlimited extraction. - **High-Quality Data Extraction**: Ensures reliable and accurate data retrieval from Gong's platform. - **User-Friendly Configuration**: Simple setup with intuitive input parameters. - **Versatile Output Formats**: Export results in JSON, CSV, or Excel formats. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------| | Keywords | array | No | The keywords for the spider. | `["revenue", "webinar", "AI"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Keywords": ["revenue", "webinar", "AI"], "item_limit": 10 } ```` #### Output JSON ```json [ { "category": "Webinars", "webinar_title": "AI is rewriting the revenue role. Hereโ€™s what comes next.", "date_of_webinar": "2026-06-25T17:00:00.000Z", "reading_duration": "1 min", "webinar_image": "https://cdn.sanity.io/images/ww20clc6/production/4ecb5fb311fa95b06fab6f42ded39992605d1ce7-1200x630.png", "webinar_details": "AI is taking over notetaking, data entry, and research. But the organizations going deepest on AI aren't cutting headcount; they're rethinking what revenue roles actually do. Join us June 25 to hear what high-performing teams are doing right now to redesign roles, build new skills, and stay ahead.", "actor_id": "ApIGg6zNa1sbPLfuK", "run_id": "QLmAA52PDCMjgPHEu" }, { "category": "Article", "webinar_title": "CEOs using AI to cut headcount are playing the wrong game", "date_of_webinar": "2026-06-04", "reading_duration": "1 min", "webinar_image": "https://cdn.sanity.io/images/ww20clc6/production/f96498fd13773f0d93ffbf772d7865cbba169fad-2616x1557.png", "webinar_details": "Gong's CEO Amit Bendov challenges the most common narrative about AI and jobs โ€” and backs it up with data.", "actor_id": "ApIGg6zNa1sbPLfuK", "run_id": "QLmAA52PDCMjgPHEu" }, { "category": "Article", "webinar_title": "The ascending impact of AI: a three-level framework", "date_of_webinar": "2026-06-01", "reading_duration": "1 min", "webinar_image": "https://cdn.sanity.io/images/ww20clc6/production/1e03db1ce48288ef273b4a96477c5763782c58b5-1999x1190.png", "webinar_details": "Gong's Craig Hanson walks us through the framework he designed to help enterprise leaders think about whatโ€™s possible with AI and design their vision.", "actor_id": "ApIGg6zNa1sbPLfuK", "run_id": "QLmAA52PDCMjgPHEu" } ] ``` ### Use Cases - **Market Research and Analysis**: Identify trends and insights from Gong's webinars and articles. - **Competitive Intelligence**: Monitor competitors' activities and strategies through their content. - **Price Monitoring**: Track pricing strategies discussed in relevant webinars or articles. - **Content Aggregation**: Compile a comprehensive list of resources for further analysis. - **Academic Research**: Gather data for studies on AI, revenue roles, and business strategies. - **Business Automation**: Automate the collection of industry insights to inform decision-making. ### Installation and Usage 1. Search for "Gong 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 containing objects with the following fields: - **category**: The type of content (e.g., Webinars, Article). - **webinar\_title**: Title of the webinar or article. - **date\_of\_webinar**: Scheduled date and time for webinars. - **reading\_duration**: Estimated reading time for articles. - **webinar\_image**: URL to the image associated with the content. - **webinar\_details**: Detailed description of the webinar or article. - **actor\_id**: Unique identifier for the actor. - **run\_id**: Unique identifier for the run. ### Error Handling The spider is designed to handle common errors gracefully. If an error occurs, it will be logged in the Apify Console. Users can review these logs to diagnose and resolve issues. Common errors include network timeouts or changes in the target website's structure. ### Rate Limiting and Best Practices To ensure optimal performance and avoid being blocked by Gong's platform: - **Respect Rate Limits**: Configure delays between requests if necessary. - **Use Proxies**: Consider using proxies for large-scale scraping to distribute requests. - **Monitor Logs**: Regularly check logs for any rate-limiting warnings or errors. ### Limitations and Considerations - The spider relies on the structure of Gong's website, which may change over time. - Ensure compliance with Gong's terms of service when using this tool. - For extensive scraping tasks, consider potential legal implications and obtain necessary permissions. # 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": [ "revenue", "webinar", "AI" ], "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/gong-discovery-parser-spider").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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/gong-discovery-parser-spider").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call getdataforme/gong-discovery-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/gong-discovery-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Gong Discovery Parser Spider", "description": "The Gong Discovery Parser Spider is a web scraping tool designed to extract data from Gongโ€™s platform based on specified keywords....", "version": "0.0", "x-build-id": "ygnVZLwk7epLsgaat" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~gong-discovery-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-gong-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~gong-discovery-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-gong-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~gong-discovery-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-gong-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "revenue", "webinar", "AI" ], "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 } } } } } } } } } } ```