# Pinterest Autocomplete Actor (`scraperoka/pinterest-autocomplete-actor`) Actor 🎭 Search “Actor” on Pinterest with Autocomplete—discover trending cast names, roles, and related keywords faster. 🚀 Great for casting calls, fan research, and creative inspiration. Save time, spark ideas, and boost your discovery! - **URL**: https://apify.com/scraperoka/pinterest-autocomplete-actor.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Social media, Lead generation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ### Pinterest Autocomplete Scraper 🚀 Manually collecting Pinterest keyword ideas from autocomplete suggestions wastes time you could spend building campaigns. **Pinterest Autocomplete Scraper** pulls autocomplete suggestions for your query in bulk, giving marketers, keyword researchers, and growth teams a fast way to mine Pinterest autocomplete keyword opportunities—often producing thousands of suggestions in minutes. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json [ { "query": "apple watch", "suggestion_01": "apple watch series", "suggestion_02": "apple watch bands", "suggestion_03": "apple watch wallpaper", "suggestion_04": "apple watch case", "suggestion_05": "apple watch deals" }, { "query": "a apple watch", "suggestion_01": "a apple watch series", "suggestion_02": "a apple watch bands", "suggestion_03": "a apple watch wallpaper", "suggestion_04": "a apple watch case", "suggestion_05": "a apple watch deals" } ] ```` **Output Fields** | Field | Type | What It Tells You | |---|---|---| | `query` | string | The exact query string that was used to fetch suggestions (including any prefix/suffix you enabled). | | `suggestion_01` | string | The first autocomplete suggestion returned for that query. | | `suggestion_02` | string | The second autocomplete suggestion returned for that query. | | `suggestion_03` | string | The third autocomplete suggestion returned for that query. | | `suggestion_04` | string | Another suggestion candidate you can use for keyword research. | | `suggestion_05` | string | A further suggestion candidate—handy for building long-tail keyword lists. | | `suggestion_06` | string | (May be present depending on `max_results`.) Additional suggestion for expanding content ideas. | | `suggestion_07` | string | (May be present depending on `max_results`.) More autocomplete mining for Pinterest keyword research. | | `suggestion_08` | string | (May be present depending on `max_results`.) Useful for content clustering. | | `suggestion_09` | string | (May be present depending on `max_results`.) Helps expand a query set quickly. | | `suggestion_10` | string | (May be present depending on `max_results`.) Captures up to the top suggestions for each query. | | `result` | (charged event name) | The run pushes records using `charged_event_name="result"` so you can track outcome by result charging. | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Pinterest Autocomplete Scraper? There are a lot of ways to pull keyword ideas from Pinterest autocomplete—here's what sets Pinterest Autocomplete Scraper apart. #### Bulk suggestions for every query you give it Pinterest Autocomplete Scraper fetches autocomplete suggestions for your `query`, then (optionally) generates additional queries using alphabetic prefixes and/or suffixes. This makes it a practical Pinterest search autocomplete scraper for keyword expansion and Pinterest autocomplete mining. #### Built for keyword research workflows The output is structured per input query, with suggestions mapped into `suggestion_01`, `suggestion_02`, and so on up to your `max_results`. That format is convenient for turning autocomplete keyword suggestions scraper output into spreadsheets, analysis, and topic clusters. #### Resilient data collection The actor is designed to keep going even when a request fails for a particular query. If something goes wrong for a query, it returns an empty suggestions list for that query rather than stopping the entire run—helpful for large-scale Pinterest autocomplete data scraper runs. #### Output is written as clean records to your dataset Results are accumulated into `all_suggestions` and pushed to the default dataset as records. This means your Pinterest autocomplete keyword extractor output is easy to export and integrate into your existing workflow. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "query": "apple watch", "max_results": 10, "use_prefix": false, "use_suffix": false } ``` | Parameter | Required | What It Does | |---|---:|---| | `query` | ✅ | The search term to get autocomplete suggestions for (for example, a product type or topic you want to target). | | `max_results` | ⬜ | The maximum number of suggestions to return for each query. | | `use_prefix` | ⬜ | If enabled, the actor adds alphabetic prefixes to your original query and fetches suggestions for each prefixed variant. | | `use_suffix` | ⬜ | If enabled, the actor adds alphabetic suffixes to your original query and fetches suggestions for each suffixed variant. | *** ### Core Capabilities #### Pinterest autocomplete keyword mining Pinterest Autocomplete Scraper is a dedicated Pinterest autocomplete scraper tool that retrieves autocomplete suggestions for your chosen query. It’s a quick way to expand beyond a single term into multiple related search suggestions. #### Flexible expansion with prefixes and suffixes With `use_prefix` and `use_suffix`, you can automatically generate more variations from your base query. This is especially useful when you want Pinterest autocomplete long tail keywords and a larger candidate pool for content planning. #### Controlled output size per query The `max_results` input caps how many suggestions you get per query. That makes it easier to keep outputs manageable while still collecting enough data for analysis. #### Run output in consistent, dataset-ready records For each query (original and any generated variants), the actor produces one record with `query` plus `suggestion_01` through `suggestion_XX`. This structured output makes it straightforward to process results in spreadsheets or analytics tools. #### Handles empty results gracefully If a suggestions request fails for a specific query, the actor returns an empty list for that query rather than stopping everything. This makes the Pinterest keyword suggestions scraper approach more reliable for bulk runs. Overall, Pinterest Autocomplete Scraper turns autocomplete-driven discovery into structured data you can act on. *** ### Who Gets the Most Out of This Here’s how different teams put Pinterest Autocomplete Scraper to work: **Pinterest SEO specialists and keyword researchers** — Use it as a Pinterest related searches scraper to quickly expand one seed term into a structured list of autocomplete keyword suggestions, then turn those into content calendars and board strategies. **Performance marketers and growth teams** — Feed the generated query variations into campaign planning. You can move from a small list of topics to a larger set of keyword ideas faster, including Pinterest autocomplete mining outputs that help diversify ad and landing page themes. **Content creators and community managers** — Build batches of “what to post next” ideas by mining autocomplete suggestions for product lines, themes, or categories. The consistent suggestion fields help you pick the strongest angles for each topic. **Freelance researchers and agencies** — Produce repeatable keyword research deliverables for clients by running the same workflow across multiple topics. This keeps your Pinterest search autocomplete scraper output consistent across projects. **Automation and data-focused builders** — Treat it as a Pinterest autocomplete API scraper pattern: trigger runs programmatically (via Apify’s platform tooling) and feed results directly into downstream data pipelines for enrichment and reporting. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Pinterest Autocomplete Scraper from start to finish: 1. **Open the actor on Apify** — visit [console.apify.com](https://console.apify.com) and open Pinterest Autocomplete Scraper. 2. **Enter your inputs** — set `query`, optionally adjust `max_results`, and choose whether to enable `use_prefix` and/or `use_suffix`. 3. **Configure proxy settings (optional)** — if you’re running larger batches, set up proxy preferences in the Apify UI for reliability. 4. **Hit Run and watch the live log** — confirm suggestions are being fetched and see the run progress. 5. **View results in the dataset tab** — each record includes `query` and `suggestion_01` up to your configured `max_results`. 6. **Export as JSON, CSV, or Excel** — download the dataset in the format that best fits your Pinterest autocomplete keyword extractor workflow. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, Pinterest Autocomplete Scraper plugs directly into your existing workflow. Export your dataset in the Apify dashboard as JSON, CSV, or Excel—ideal for teams doing keyword research analysis, reporting, or content planning. For automation and integration use cases, you can connect results to other tools using Apify’s API access and dataset integrations. For deeper details, refer to the Apify documentation: https://apify.com/docs/api *** ### Pricing & Free Trial Pinterest Autocomplete Scraper runs on the Apify platform, which offers a **free tier** — no credit card required to get started. From there, usage is typically pay-as-you-go based on Apify platform compute (CU). You can scale up for larger Pinterest autocomplete data scraper jobs without changing your workflow—just run with updated inputs and export your dataset when ready. Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Rate-limit pressure | Designed to continue collecting suggestions across multiple queries. | | Scraping stability | Built with resilient request handling so partial failures don’t break the entire run. | | Output completeness | Results are pushed as records to your default dataset for straightforward export. | | Controlled volume | `max_results` caps how many suggestions you receive per query. | | Bulk expansion | Optional `use_prefix` / `use_suffix` expands query coverage automatically. | | Failure behavior | If a specific query fetch fails, that query returns no suggestions rather than stopping the run. | Limitations: This actor focuses on Pinterest autocomplete suggestions for a given query and returns what’s available via that autocomplete mechanism. If suggestions are sparse or unavailable for a particular query variant, the corresponding record may contain fewer suggestions. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial for Pinterest Autocomplete Scraper? Yes—Apify offers a free tier with platform credits so you can run Pinterest Autocomplete Scraper for testing before committing to a larger workflow. #### Do I need to log in to Pinterest to use this? No. Pinterest Autocomplete Scraper is designed to scrape autocomplete suggestions from publicly available sources without requiring you to log in. #### How accurate is the data? The suggestions returned are the autocomplete suggestions associated with the queries you provide. Accuracy depends on what autocomplete offers at the time of the run. #### How many results can I get per run? You control the maximum number of suggestions returned per query using `max_results`. If you enable `use_prefix` or `use_suffix`, the actor will run additional generated queries as well, producing one record per query variant. #### How often is the data updated / how fresh is it? Freshness depends on when you run the actor. The actor fetches autocomplete suggestions during the run, so results reflect the current autocomplete state at that time. #### Is this legal? Does it comply with GDPR / CCPA? The actor collects data from **publicly available data** exposed via autocomplete. It’s still your responsibility to ensure your use complies with GDPR, CCPA, and any applicable platform terms and local regulations. #### Can I export results to Google Sheets or Excel? Yes. You can export your dataset from the Apify dashboard, and CSV/Excel formats are typically easiest for Google Sheets and spreadsheet workflows. #### Can I run this on a schedule automatically? Yes. Apify supports scheduling via its platform capabilities, so you can automate Pinterest autocomplete keyword research runs on a recurring basis. #### Can I access this via API? Yes. You can run the actor and retrieve dataset outputs programmatically using the Apify API. See https://apify.com/docs/api #### What happens if the actor hits an error? If a suggestions request fails for a particular query, the actor returns an empty suggestions list for that query while continuing the run. This helps prevent one failure from stopping all Pinterest autocomplete scraper output. *** ### Need Help or Have a Request? Got a question about Pinterest Autocomplete Scraper or want a new feature added? Reach out at . We actively maintain this actor based on user feedback, and we’re open to practical improvements like broader query expansion options and run-completion webhooks. *** ### Disclaimer & Responsible Use *Pinterest Autocomplete Scraper is the fastest, most reliable way to collect Pinterest autocomplete suggestions—start your free run today.* This actor collects **publicly available data**. It does not access private accounts, login-gated content, or password-protected pages. You are responsible for ensuring your usage complies with GDPR, CCPA, platform terms, and any applicable local regulations. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `query` (type: `string`): The search term to get suggestions for. ## `max_results` (type: `integer`): The maximum number of suggestions to return for each query. ## `use_prefix` (type: `boolean`): Whether to add alphabetic prefixes to the query. ## `use_suffix` (type: `boolean`): Whether to add alphabetic suffixes to the query. ## Actor input object example ```json { "query": "apple watch", "max_results": 10, "use_prefix": false, "use_suffix": false } ``` # 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("scraperoka/pinterest-autocomplete-actor").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("scraperoka/pinterest-autocomplete-actor").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 scraperoka/pinterest-autocomplete-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/pinterest-autocomplete-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pinterest Autocomplete Actor", "description": "🎭 Search “Actor” on Pinterest with Autocomplete—discover trending cast names, roles, and related keywords faster. 🚀 Great for casting calls, fan research, and creative inspiration. Save time, spark ideas, and boost your discovery!", "version": "1.0", "x-build-id": "pwjv5akNa7hKyasTw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~pinterest-autocomplete-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-pinterest-autocomplete-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~pinterest-autocomplete-actor/runs": { "post": { "operationId": "runs-sync-scraperoka-pinterest-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~pinterest-autocomplete-actor/run-sync": { "post": { "operationId": "run-sync-scraperoka-pinterest-autocomplete-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Query", "type": "string", "description": "The search term to get suggestions for.", "default": "apple watch" }, "max_results": { "title": "Max Results", "type": "integer", "description": "The maximum number of suggestions to return for each query.", "default": 10 }, "use_prefix": { "title": "Use Prefix", "type": "boolean", "description": "Whether to add alphabetic prefixes to the query.", "default": false }, "use_suffix": { "title": "Use Suffix", "type": "boolean", "description": "Whether to add alphabetic suffixes to the query.", "default": false } } }, "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 } } } } } } } } } } ```