# DTC Bike Brand Catalog & Pricing Aggregator (`jungle_synthesizer/dtc-bike-brand-catalog-pricing-aggregator-scraper`) Actor Scrape bike product listings, specs, and current pricing from Canyon, Trek, and Felt direct-to-consumer brand sites. Delivers model name, category, SKU, price, stock status, and spec data. - **URL**: https://apify.com/jungle\_synthesizer/dtc-bike-brand-catalog-pricing-aggregator-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce, Sports - **Stats:** 2 total users, 1 monthly users, 33.3% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## DTC Bike Brand Catalog & Pricing Aggregator Scrapes product listings, specifications, and current pricing from three direct-to-consumer bicycle brand websites: **Canyon**, **Trek**, and **Felt**. Returns a unified dataset of bike models with prices, stock status, frame material, drivetrain, and available size/color options. ### What it does The scraper visits the official brand stores and extracts current catalog data per bike model: - **Canyon** (Salesforce Commerce Cloud) — category listing pages feed product detail URLs; per-product data is extracted from ProductGroup JSON-LD on the detail pages - **Trek** (SAP Hybris with Vue SSR) — category listing pages carry complete product data in Vue component attributes; no detail-page hop required - **Felt** (Shopify) — collection listing pages route to product detail pages where Shopify's ProductGroup JSON-LD is parsed ### Output fields | Field | Type | Description | |-------|------|-------------| | `brand` | string | Source brand: `canyon`, `trek`, or `felt` | | `model_name` | string | Full model name (e.g. "Aeroad CFR Di2") | | `model_year` | number | Model year extracted from name/tags | | `category` | string | `road`, `gravel`, `mtb`, `ebike`, or `urban` | | `sub_category` | string | Sub-category slug from URL path | | `sku` | string | Product SKU or handle | | `frame_material` | string | `carbon`, `aluminum`, `steel`, or `titanium` | | `groupset` | string | Drivetrain groupset inferred from specs/name | | `wheelset` | string | Wheelset specification (when available) | | `weight_kg` | number | Bike weight in kg (when declared in specs) | | `frame_size_options` | string | Available frame sizes, comma-separated | | `color_options` | string | Available colors, comma-separated | | `price_usd` | number | Current retail price in USD | | `price_msrp_usd` | number | MSRP/original price when different from sale price | | `in_stock` | boolean | Current stock status | | `discontinued` | boolean | Whether the model is discontinued | | `product_url` | string | Canonical product page URL | | `image_url` | string | Primary product image URL | | `description` | string | Product description (up to 500 characters) | | `spec_sheet` | string | JSON string of key component specs (Canyon only) | ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum total records to return | | `brands` | array | `["canyon", "trek", "felt"]` | Which brands to include | | `categories` | array | `[]` | Filter by category slug (empty = all) | ### Notes - Canyon products include a `spec_sheet` JSON field with detailed component specs from their ProductGroup JSON-LD. - Trek product detail pages are JavaScript-rendered (SPA). All available data — name, price, category, stock status — is extracted from server-side-rendered listing page attributes, so Trek records do not include spec sheets or weight. - Felt product pages are Shopify-hosted and return a full ProductGroup JSON-LD; spec extraction is limited to what Shopify exposes in structured data. - Pricing data reflects the public retail price at the time of scraping. Sale prices are captured in `price_usd`; if a strike-through MSRP is present, it appears in `price_msrp_usd`. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of bike records to scrape (across all brands) ## `brands` (type: `array`): Which brands to include in this run ## `categories` (type: `array`): Filter by category (road, gravel, mtb, ebike, urban). Leave empty for all. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brands": [ "canyon", "trek", "felt" ], "categories": [] } ```` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brands": [ "canyon", "trek", "felt" ], "categories": [] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/dtc-bike-brand-catalog-pricing-aggregator-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brands": [ "canyon", "trek", "felt", ], "categories": [], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/dtc-bike-brand-catalog-pricing-aggregator-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "brands": [ "canyon", "trek", "felt" ], "categories": [] }' | apify call jungle_synthesizer/dtc-bike-brand-catalog-pricing-aggregator-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/dtc-bike-brand-catalog-pricing-aggregator-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DTC Bike Brand Catalog & Pricing Aggregator", "description": "Scrape bike product listings, specs, and current pricing from Canyon, Trek, and Felt direct-to-consumer brand sites. Delivers model name, category, SKU, price, stock status, and spec data.", "version": "0.1", "x-build-id": "xYxJg5BhbrUDeDYIB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~dtc-bike-brand-catalog-pricing-aggregator-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-dtc-bike-brand-catalog-pricing-aggregator-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/jungle_synthesizer~dtc-bike-brand-catalog-pricing-aggregator-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-dtc-bike-brand-catalog-pricing-aggregator-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/jungle_synthesizer~dtc-bike-brand-catalog-pricing-aggregator-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-dtc-bike-brand-catalog-pricing-aggregator-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", "required": [ "maxItems", "brands" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of bike records to scrape (across all brands)", "default": 10 }, "brands": { "title": "Brands to Scrape", "type": "array", "description": "Which brands to include in this run", "default": [ "canyon", "trek", "felt" ], "items": { "type": "string" } }, "categories": { "title": "Bike Categories", "type": "array", "description": "Filter by category (road, gravel, mtb, ebike, urban). Leave empty for all.", "default": [], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```