# Pinterest Autocomplete Scraper (`scrapevanta/pinterest-autocomplete-scraper`) Actor 🎯 Pinterest Autocomplete Scraper extracts keyword suggestions & autocomplete data fast. 🚀 Perfect for Pinterest SEO, content planning, and keyword research. 📌 Automate insights for higher visibility and smarter strategy! - **URL**: https://apify.com/scrapevanta/pinterest-autocomplete-scraper.md - **Developed by:** [ScrapeVanta](https://apify.com/scrapevanta) (community) - **Categories:** SEO tools, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 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 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 ### Pinterest Autocomplete Scraper 🚀 Getting Pinterest keyword ideas the hard way—one slow suggestion at a time—is a major time sink for marketers and researchers. **Pinterest Autocomplete Scraper** pulls autocomplete suggestions for your query so you can harvest keyword variations faster. It’s a practical Pinterest autocomplete scraper for keyword autocomplete data scraping, including autocomplete suggestions downloader-style workflows. Built for SEO teams, content strategists, and analysts who want more coverage from less manual effort. In a single run, you can collect suggestions for your main term and (optionally) many prefix/suffix variations in one go—then export immediately. ### See the Data: Sample Output Here's a real record from a single run: ```json { "query": "apple watch", "suggestion_01": "apple watch series", "suggestion_02": "apple watch bands", "suggestion_03": "apple watch deals", "suggestion_04": "apple watch size", "suggestion_05": "apple watch setup" } ```` | Field | Type | What It Tells You | |---|---|---| | `query` | string | The input term (or generated prefix/suffix term) used to request suggestions. | | `suggestion_01` | string | One of the top autocomplete suggestions returned for that query (ranked). | | `suggestion_02` | string | A next suggestion that can become a new keyword idea. | | `suggestion_03` | string | Another suggestion to expand your keyword autocomplete harvesting. | | `suggestion_04` | string | Helps you gather more related search suggestions beyond your original phrase. | | `suggestion_05` | string | Additional suggestion (you’ll get up to your configured max results). | | `suggestion_06` | string | More coverage for Pinterest autocomplete keyword harvesting. | | `suggestion_07` | string | Another suggestion for building clusters around one topic. | | `suggestion_08` | string | Adds more variety for content planning and ad group ideas. | | `suggestion_09` | string | Useful for expanding “people also search for” style research. | | `suggestion_10` | string | The 10th suggestion if available (based on the actor’s request). | | `status` | string | Use this to track whether a record is usable in your downstream pipeline (note: this actor writes results via `charged_event_name="result"`; no explicit `status` field is defined in the code). | | `error_message` | string | If a request fails, the actor returns an empty list for that query; use your own ETL logic to treat missing suggestions as “no data.” (No explicit `error_message` field is written by the actor.) | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "query": "apple watch", "max_results": 10, "use_prefix": false, "use_suffix": false } ``` | Parameter | Required | What It Does | |---|---|---| | `query` | ✅ | The search term to get suggestions for. | | `max_results` | ⬜ | The maximum number of suggestions to return for each query. | | `use_prefix` | ⬜ | Whether to add alphabetic prefixes to the query (expands your Pinterest autocomplete search suggestions coverage). | | `use_suffix` | ⬜ | Whether to add alphabetic suffixes to the query (expands your Pinterest search autocomplete scraper results). | ### What It Does This actor scrapes Pinterest autocomplete suggestions for a given query and saves them as structured records in your Apify dataset. #### Collects autocomplete suggestions per query For your provided `query`, it requests a set of suggestions and stores them as `suggestion_01`, `suggestion_02`, and so on. This makes the Pinterest autocomplete extractor output easy to plug into spreadsheets or keyword research workflows. #### Optionally expands coverage with prefixes and suffixes If you enable `use_prefix` and/or `use_suffix`, the actor generates additional query variants by adding alphabetic prefixes or suffixes. This is useful when you want more exhaustive Pinterest autocomplete keyword harvesting than a single keyword run provides. #### Produces a consistent, integration-friendly dataset Each record includes the `query` plus numbered suggestion fields, so you can reliably map results into your analysis pipeline. The output is written to the default dataset via `await Actor.push_data(..., charged_event_name="result")`. #### Includes resilience when requests fail If a request fails for a specific query, the actor logs the error and returns an empty suggestion list for that query instead of crashing the whole run. That behavior helps keep larger harvesting jobs moving forward. Overall, this Pinterest Autocomplete Scraper turns autocomplete suggestions into clean, exportable data you can analyze and act on quickly. ### Why Pinterest Autocomplete Scraper? There are plenty of ways to pull data from Pinterest autocomplete—here’s why Pinterest Autocomplete Scraper stands out. #### Structured output built for keyword research Instead of reading suggestions manually, the Pinterest Autocomplete Scraper returns each query with consistent numbered fields (`suggestion_01` …). That structure is exactly what you need for autocomplete suggestions downloader-style analysis and clustering. #### Scales coverage with prefix/suffix expansion With `use_prefix` and `use_suffix`, Pinterest autocomplete scraping script style workflows become straightforward: one run can gather suggestions for many related query variations. This helps you avoid missing long-tail related phrases. #### Keeps the run productive even when some queries fail When a request errors, the actor returns an empty list for that query and continues. That makes the Pinterest autocomplete tool more practical for bulk keyword exploration where occasional failures can happen. ### Real-World Use Cases Here's how different teams put Pinterest Autocomplete Scraper to work: **SEO Content Strategists**\ A content planner starts with a core theme (for example, “apple watch”) and needs related search variations for headlines and clusters. They run Pinterest Autocomplete Scraper once, then export results to expand their topic map without spending hours checking suggestions one by one. **Growth Marketers**\ A marketer researching ad angles uses autocomplete suggestions to generate new keyword targets and write tighter creatives. By turning Pinterest autocomplete suggestions into structured data, they can quickly compare clusters and prioritize what to test next. **Freelance Researchers**\ A consultant delivers keyword coverage reports to clients who want “what people actually type next.” The researcher uses the Pinterest autocomplete extractor output as a repeatable step in every project, then exports the dataset for client-ready reporting. **Data Analysts**\ An analyst wants to quantify how suggestion variety changes across query styles. They run Pinterest keyword autocomplete scraper scenarios (with and without prefix/suffix expansion), then model the suggestion sets over time using the structured `query` and `suggestion_*` fields. **Automation & Integration Specialists**\ An automation specialist builds a pipeline that triggers a job, ingests results, and updates a keyword database. Because the actor outputs consistent records into the dataset, it’s easy to connect into your downstream workflow and keep runs hands-off. ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and find Pinterest Autocomplete Scraper. 2. **Enter your inputs** — set `query` (required), optionally tune `max_results`, and choose whether to enable `use_prefix` and/or `use_suffix`. 3. **Configure proxy settings (optional)** — if you use proxy configuration in your Apify projects, enable it according to your usual settings for more reliable scraping. 4. **Start the run and watch the live log** — track progress and see suggestion counts for each query in the actor logs. 5. **Open the Dataset tab** — records appear as the run completes, with `query` and `suggestion_01..suggestion_10` fields. 6. **Export in your preferred format** — download the dataset as JSON, CSV, or Excel from the Apify dashboard. 7. **Iterate for broader coverage** — if you need more keywords, rerun with `use_prefix`/`use_suffix` enabled. The whole setup takes under 5 minutes — results start appearing within seconds of launch. ### Export & Integration Options Once your data is collected, Pinterest Autocomplete Scraper fits directly into your existing workflow. You can download results from the Apify dataset tab in **JSON**, **CSV**, or **Excel**, which makes it simple to move from scraping into analysis or reporting. If you’re building lists of Pinterest autocomplete data scraping outputs, these export options are usually all you need. For automation and system integration, you can connect workflows using **API access**, **webhooks**, and **Zapier / Make** style no-code automation supported by Apify. You can also run scheduled jobs to keep your Pinterest keyword autocomplete scraper dataset fresh without manual effort. ### Pricing Pinterest Autocomplete Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. Free tier typically includes **$5 platform credits** on sign-up, enough for several real test runs. For heavier harvesting, Apify supports pay-as-you-go billing based on Actor compute units (CU), plus subscription plans for ongoing workloads. Start free at [apify.com](https://apify.com) — scale up when you need. ### Reliability & Limitations | What We Handle | How | |---|---| | Partial failures | If a request fails for a query, the actor logs the error and returns an empty list for that query. | | Output consistency | Results are written as structured records with `query` and `suggestion_*` fields. | | Scale-friendly runs | Bulk harvesting is supported by generating additional query variants when `use_prefix` or `use_suffix` is enabled. | **Limitations:** This actor focuses on autocomplete suggestions for a given `query` (and optional prefix/suffix variants). It does not fetch account-specific content, and the richness of results depends on what publicly returns suggestions for the requested terms. For enterprise-scale needs or custom configurations, reach out and we'll help. ### Frequently Asked Questions #### Is there a free plan? Apify offers a free tier with platform credits on sign-up. Whether your run fits in the free allowance depends on your input and how many query variants you generate. #### Do I need to log in or create an account on Pinterest? No. This actor works by scraping publicly available data for autocomplete suggestions—no Pinterest login is required by the actor. #### How accurate is the extracted data? The output reflects the autocomplete suggestions returned for the requested `query` values. The actor does not attempt to guess or “correct” suggestions—it captures what the suggestions endpoint returns for each term. #### How many results can I get per run? You can control the number of suggestions per query with `max_results`. If you keep the default `max_results` of 10 and don’t enable prefix/suffix expansion, the run returns suggestions for just the original `query` record. #### How fresh is the data? The actor collects suggestions during the run, so the dataset reflects autocomplete suggestions at the time you execute the job. For the freshest keyword variations, rerun periodically. #### Is this legal? Does it comply with GDPR / CCPA? The actor only collects **publicly available data** used for autocomplete suggestions. You’re responsible for ensuring your usage complies with GDPR, CCPA, applicable platform terms, and local regulations for data handling. #### Can I export to Google Sheets or Excel? Yes. You can export from the Apify dashboard as JSON, CSV, or Excel. Then import the file into Google Sheets or any tool that accepts CSV/Excel. #### Can I schedule this to run automatically? Yes. Apify supports scheduled runs, so you can automate repeated Pinterest autocomplete keyword harvesting as part of your ongoing SEO workflow. #### Can I access results via the API? Yes. Since results are written to the Apify dataset, you can retrieve them programmatically using the Apify API. #### What happens when the actor encounters an error? If the request fails for a specific query, the actor logs the error and returns an empty suggestion list for that query. The run continues so you still get results for other generated query variants (if enabled). ### Get Help & Use Responsibly Got a question about Pinterest Autocomplete Scraper or a feature you'd like added? Reach out at . We welcome feedback and can help you apply this Pinterest autocomplete scraper to your workflow. **This actor collects publicly available data** and does not access private accounts, login-gated pages, or password-protected content. You are responsible for complying with GDPR, CCPA, and Pinterest’s Terms of Service when using and storing the data. 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("scrapevanta/pinterest-autocomplete-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 = {} # Run the Actor and wait for it to finish run = client.actor("scrapevanta/pinterest-autocomplete-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 '{}' | apify call scrapevanta/pinterest-autocomplete-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapevanta/pinterest-autocomplete-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pinterest Autocomplete Scraper", "description": "🎯 Pinterest Autocomplete Scraper extracts keyword suggestions & autocomplete data fast. 🚀 Perfect for Pinterest SEO, content planning, and keyword research. 📌 Automate insights for higher visibility and smarter strategy!", "version": "1.0", "x-build-id": "7axlgla3cq1K9wbHE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapevanta~pinterest-autocomplete-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapevanta-pinterest-autocomplete-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapevanta~pinterest-autocomplete-scraper/runs": { "post": { "operationId": "runs-sync-scrapevanta-pinterest-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapevanta~pinterest-autocomplete-scraper/run-sync": { "post": { "operationId": "run-sync-scrapevanta-pinterest-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "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 } } } } } } } } } } ```