# Pinterest Bulk Autocomplete Keyword Scraper (`dt_org/pinterest-keyword-scraper`) Actor Pinterest Keyword Autocomplete Scraper pulls the exact autocomplete suggestions Pinterest shows for any search term. Feed it up to 1000 keywords in bulk and get back every suggested term for each one, ready for keyword research, content planning, or SEO work at scale.. - **URL**: https://apify.com/dt\_org/pinterest-keyword-scraper.md - **Developed by:** [DreamTeam](https://apify.com/dt_org) (community) - **Categories:** Lead generation, SEO tools, Social media - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 one suggestion 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 ### What is Pinterest Keyword Autocomplete Scraper? **Pinterest Keyword Autocomplete Scraper** pulls the exact autocomplete suggestions Pinterest shows for any search term — the same suggestions you'd see typing into the Pinterest search bar yourself. Feed it a list of keywords and get back every suggested term Pinterest returns for each one. It's built to be lightweight: no browser, no headless Chrome — just direct requests against Pinterest's own endpoints, so runs finish fast and don't burn unnecessary compute. ### Key features - 📦 **Bulk keyword input** — submit up to 1000 keywords in a single run - ⚡ **Lightweight and fast** — request-based, no browser overhead - 🔢 **Ranked suggestions** — each result keeps its original position in Pinterest's suggestion list - 🕒 **Timestamped results** — every row records exactly when it was scraped ### Data fields returned | Field | Description | |---|---| | `query_keyword` | The keyword you searched for | | `index` | Position of the suggestion in Pinterest's autocomplete list | | `suggested_keyword` | The autocomplete suggestion Pinterest returned | | `datetime_scraped_utc` | Date and time the suggestion was scraped (UTC) | | `unixtime_scraped` | Unix timestamp of the scrape | ### Why use this scraper? - 🔑 Expand seed keywords into hundreds of related search terms - 📈 Feed keyword research or SEO tools with real Pinterest search behavior - 🗂 Plan content and boards around what people are actually searching for - 🤖 Automate keyword discovery at scale instead of typing terms one by one ### Input ```json { "keywords": ["cars", "home decor", "birthday cake"] } ```` Up to 1000 keywords per run. ### Output sample ```json [ { "query_keyword": "restaurant food", "index": 1, "suggested_keyword": "restaurant food snap", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 2, "suggested_keyword": "restaurant food photography", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 3, "suggested_keyword": "restaurant food pics", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 4, "suggested_keyword": "restaurant food video", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 5, "suggested_keyword": "restaurant food snap night", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 6, "suggested_keyword": "restaurant food poster", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 7, "suggested_keyword": "restaurant foods", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 8, "suggested_keyword": "restaurant food ideas", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 9, "suggested_keyword": "restaurant food pictures", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 10, "suggested_keyword": "restaurant food snap with friends", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 11, "suggested_keyword": "restaurant food design", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "restaurant food", "index": 12, "suggested_keyword": "restaurant food aestethics", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 1, "suggested_keyword": "retro cars vintage", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 2, "suggested_keyword": "retro cars aesthetic", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 3, "suggested_keyword": "retro cars wallpaper", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 4, "suggested_keyword": "retro cars vintage aesthetic wallpaper", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 5, "suggested_keyword": "retro cars vintage wallpaper 4k", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 6, "suggested_keyword": "retro cars wallpaper 4k", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 7, "suggested_keyword": "retro cars poster", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 8, "suggested_keyword": "retro cars drawing", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 9, "suggested_keyword": "retro cars vintage wallpaper", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 10, "suggested_keyword": "retro cars vintage aesthetic", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 11, "suggested_keyword": "retro cars vintage poster", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 }, { "query_keyword": "retro cars", "index": 12, "suggested_keyword": "retro cars wallpaper laptop", "datetime_scraped_utc": "2026-07-05 07:29:49", "unixtime_scraped": 1783236589 } ] ``` ### Related 📌 Need full pin details instead of just keyword suggestions — images, boards, pinners, videos? Check out our [Pinterest Scraper](https://apify.com/dt_org/pinterest-scraper-by-search-query-detailed-output). ### Feedback Found a bug or have a feature request? Open an issue on this Actor's **Issues** tab in Apify Console. # Actor input Schema ## `keywords` (type: `array`): List of keywords to get Pinterest autocomplete suggestions for. Up to 1000 keywords per run. ## Actor input object example ```json { "keywords": [ "retro cars" ] } ``` # 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 = { "keywords": [ "retro cars" ] }; // Run the Actor and wait for it to finish const run = await client.actor("dt_org/pinterest-keyword-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 = { "keywords": ["retro cars"] } # Run the Actor and wait for it to finish run = client.actor("dt_org/pinterest-keyword-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 '{ "keywords": [ "retro cars" ] }' | apify call dt_org/pinterest-keyword-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dt_org/pinterest-keyword-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pinterest Bulk Autocomplete Keyword Scraper", "description": "Pinterest Keyword Autocomplete Scraper pulls the exact autocomplete suggestions Pinterest shows for any search term. Feed it up to 1000 keywords in bulk and get back every suggested term for each one, ready for keyword research, content planning, or SEO work at scale..", "version": "0.1", "x-build-id": "cwUl8dGVoHKophvbk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dt_org~pinterest-keyword-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dt_org-pinterest-keyword-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dt_org~pinterest-keyword-scraper/runs": { "post": { "operationId": "runs-sync-dt_org-pinterest-keyword-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dt_org~pinterest-keyword-scraper/run-sync": { "post": { "operationId": "run-sync-dt_org-pinterest-keyword-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "🔍 Keywords", "type": "array", "description": "List of keywords to get Pinterest autocomplete suggestions for. Up to 1000 keywords per run.", "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 } } } } } } } } } } ```