# Thingiverse Scraper & Trend Extractor (`nicocomt/thingiverse-trends`) Actor 🧩 Récupérez les modèles 3D de Thingiverse en données propres ! 🔎 Recherche, 🔥 tri par likes/makes/date, 📊 export JSON/CSV/Excel. 🇬🇧 Get Thingiverse 3D models as clean data: search, sort, export. 🔑 Bring your free token. - **URL**: https://apify.com/nicocomt/thingiverse-trends.md - **Developed by:** [Nicolas](https://apify.com/nicocomt) (community) - **Categories:** E-commerce, Developer tools, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 ## 🧩 Thingiverse Scraper & Trend Extractor Extrait des modèles 3D de **Thingiverse** en données propres et structurées (JSON, CSV, Excel), avec un **score de tendance** pour repérer ce qui monte. ### 🆕 Nouveautés / Changelog #### v2.0 — actuelle / current - 🔥 **Score de tendance (velocity)** — likes par jour depuis la publication : repère ce qui monte, pas seulement les vieux hits. / Trend score (likes per day) to surface rising models. - 🔑 **Token optionnel** — plus besoin de fournir le vôtre (sauf gros volumes). / Optional token, no setup needed. - ↕️ **Option « trier par tendance »**. / "Sort by trend" option. #### v1.0 - 🔎 Recherche par mot-clé + tri (populaire, récent, réalisations). / Keyword search + sort. - 📊 Export structuré (nom, créateur, likes, collections, commentaires, makes…). / Structured export. ### 🇫🇷 Français **Ce que fait cet Actor** : interroge l'API officielle Thingiverse et renvoie une liste structurée de modèles 3D, avec un score de tendance. **Utilisation** 1. (Optionnel) Renseignez votre App Token Thingiverse, ou laissez vide. 2. Entrez un mot-clé (ou laissez vide pour les populaires). 3. Choisissez le tri, activez « trier par tendance » si besoin, réglez le nombre de résultats. 4. Lancez et exportez. **Cas d'usage** : veille sur les modèles qui montent, idées de produits imprimables, datasets 3D. ### 🇬🇧 English **What it does**: queries the official Thingiverse API and returns a structured list of 3D models with a trend score. **How to use** 1. (Optional) Provide your Thingiverse App Token, or leave empty. 2. Enter a keyword (or leave empty for popular models). 3. Pick the sort, toggle "sort by trend" if needed, set the number of results. 4. Run and export. **Use cases**: track rising 3D models, find printable product ideas, build 3D datasets. --- *Données issues de l'API publique Thingiverse. / Data from the public Thingiverse API.* # Actor input Schema ## `searchTerm` (type: `string`): Terme à rechercher (ex. « articulated dragon »). Vide = modèles populaires. / Keyword. Empty = popular models. ## `sort` (type: `string`): Ordre de tri renvoyé par Thingiverse. / Sort order from Thingiverse. ## `sortByTrend` (type: `boolean`): Trie les résultats par score de tendance (likes par jour depuis publication). / Sort by trend score (likes per day). ## `maxResults` (type: `integer`): Nombre maximum de modèles (1–500). / Max models (1–500). ## `thingiverseToken` (type: `string`): Optionnel : laissez vide pour utiliser le token par défaut. Pour de gros volumes, mettez le vôtre (gratuit sur thingiverse.com/apps/create). / Optional: leave empty to use the default token. For large volumes, use your own. ## Actor input object example ```json { "searchTerm": "articulated dragon", "sort": "popular", "sortByTrend": false, "maxResults": 50 } ```` # 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 = { "searchTerm": "articulated dragon" }; // Run the Actor and wait for it to finish const run = await client.actor("nicocomt/thingiverse-trends").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 = { "searchTerm": "articulated dragon" } # Run the Actor and wait for it to finish run = client.actor("nicocomt/thingiverse-trends").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 '{ "searchTerm": "articulated dragon" }' | apify call nicocomt/thingiverse-trends --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nicocomt/thingiverse-trends", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Thingiverse Scraper & Trend Extractor", "description": "🧩 Récupérez les modèles 3D de Thingiverse en données propres ! 🔎 Recherche, 🔥 tri par likes/makes/date, 📊 export JSON/CSV/Excel. \n🇬🇧 Get Thingiverse 3D models as clean data: search, sort, export. 🔑 Bring your free token.", "version": "0.0", "x-build-id": "AV6JdgQl1G7qFwQSL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nicocomt~thingiverse-trends/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nicocomt-thingiverse-trends", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nicocomt~thingiverse-trends/runs": { "post": { "operationId": "runs-sync-nicocomt-thingiverse-trends", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nicocomt~thingiverse-trends/run-sync": { "post": { "operationId": "run-sync-nicocomt-thingiverse-trends", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchTerm": { "title": "Mot-clé de recherche / Search keyword", "type": "string", "description": "Terme à rechercher (ex. « articulated dragon »). Vide = modèles populaires. / Keyword. Empty = popular models." }, "sort": { "title": "Tri / Sort", "enum": [ "relevant", "popular", "newest", "makes" ], "type": "string", "description": "Ordre de tri renvoyé par Thingiverse. / Sort order from Thingiverse.", "default": "popular" }, "sortByTrend": { "title": "Trier par tendance / Sort by trend", "type": "boolean", "description": "Trie les résultats par score de tendance (likes par jour depuis publication). / Sort by trend score (likes per day).", "default": false }, "maxResults": { "title": "Nombre max de résultats / Max results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Nombre maximum de modèles (1–500). / Max models (1–500).", "default": 50 }, "thingiverseToken": { "title": "Token API Thingiverse (optionnel) / Thingiverse API token (optional)", "type": "string", "description": "Optionnel : laissez vide pour utiliser le token par défaut. Pour de gros volumes, mettez le vôtre (gratuit sur thingiverse.com/apps/create). / Optional: leave empty to use the default token. For large volumes, use your own." } } }, "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 } } } } } } } } } } ```