# Pinterest Keyword Autocomplete Scraper (`dromb/pinterest-keyword-autocomplete-scraper`) Actor Discover live Pinterest autocomplete keyword ideas for Pinterest SEO, content planning, product research, and trend discovery. - **URL**: https://apify.com/dromb/pinterest-keyword-autocomplete-scraper.md - **Developed by:** [Dmitriy Gyrbu](https://apify.com/dromb) (community) - **Categories:** Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.05 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Keyword Autocomplete Scraper Extract live Pinterest autocomplete keyword suggestions from seed terms and turn them into a clean dataset for Pinterest SEO, product research, content planning, and trend discovery. This Actor is unofficial and is not affiliated with Pinterest. ### Why Use It Pinterest autocomplete is a practical demand signal for visual search, shopping inspiration, seasonal planning, and content ideas. This Actor helps you turn a few seed terms into structured keyword ideas you can export, cluster, and use in SEO, marketplace, or social content workflows. Use it to: - discover Pinterest keyword ideas for home decor, beauty, fashion, food, weddings, crafts, and lifestyle niches; - plan pins, boards, blog posts, product collections, Etsy listings, Shopify pages, and seasonal campaigns; - expand product and content topics into long-tail Pinterest search phrases; - research visual trends before creating creative assets or ad campaigns; - monitor how autocomplete suggestions change around a niche, product, or seasonal theme. ### What You Get Each result is a normalized keyword suggestion with the original seed keyword, generated lookup query, suggestion text, source, and collection timestamp. ```json { "seedKeyword": "wedding decor", "query": "wedding decor", "suggestion": "wedding decor ideas", "source": "pinterest_autocomplete", "collectedAt": "2026-07-02T00:00:00+00:00" } ```` The run also stores an `OUTPUT` summary with the run status, number of requested queries, successful queries, failed queries, pushed items, and structured errors if any query fails. ### Input ```json { "seedKeywords": ["wedding decor", "nail designs"], "expansionMode": "none", "maxQueries": 50, "maxSuggestionsPerQuery": 20, "deduplicate": true } ``` #### Input Fields - `seedKeywords`: Base terms, products, categories, styles, seasons, events, or topics to research. - `expansionMode`: Controls how seed keywords are turned into autocomplete lookups. - `maxQueries`: Maximum number of generated autocomplete lookups for the run. - `maxSuggestionsPerQuery`: Maximum number of suggestions saved per lookup. - `deduplicate`: Keeps only the first occurrence of each suggestion across the run. ### Expansion Modes #### Exact keyword discovery Set `expansionMode` to `none` when you want suggestions for the seed keywords exactly as entered. Best for quick niche checks, seasonal idea validation, and small keyword lists. ```json { "seedKeywords": ["wedding decor", "nail designs"], "expansionMode": "none", "maxQueries": 2, "maxSuggestionsPerQuery": 10, "deduplicate": true } ``` #### Long-tail alphabet expansion Set `expansionMode` to `alphabet` to generate lookups such as `home office a`, `home office b`, and so on. Best for broad long-tail Pinterest SEO research, board planning, product category expansion, and finding phrases that do not appear from a single seed query. ```json { "seedKeywords": ["home office"], "expansionMode": "alphabet", "maxQueries": 10, "maxSuggestionsPerQuery": 10, "deduplicate": true } ``` #### Inspiration and research expansion Set `expansionMode` to `questions` to generate Pinterest-friendly research lookups such as `how to keyword`, `keyword ideas`, `keyword inspiration`, `keyword diy`, and `keyword aesthetic`. Best for content calendars, creative briefs, product page ideas, seasonal campaigns, and trend research. ```json { "seedKeywords": ["summer outfits"], "expansionMode": "questions", "maxQueries": 8, "maxSuggestionsPerQuery": 10, "deduplicate": true } ``` ### How To Use 1. Enter one or more seed keywords. 2. Choose an expansion mode. 3. Keep `maxQueries` small for quick checks or raise it for broader research. 4. Run the Actor. 5. Export the dataset as JSON, CSV, Excel, or connect it to your workflow through the Apify API. ### Practical Tips - Use `none` for a fast first pass. - Use `alphabet` when you need broad long-tail coverage. - Use `questions` when planning inspiration-led content, tutorials, DIY topics, and creative campaigns. - Keep `deduplicate` enabled when you want a clean keyword list. - Disable `deduplicate` only when you need to see the same suggestion appearing under multiple generated queries. ### Limits And Notes - Suggestions can vary by time, location, language, and Pinterest's current behavior. - The Actor returns autocomplete suggestions, not search volume, competition, or ranking difficulty. - Very broad seed keywords can produce generic suggestions; specific seed terms usually produce more actionable results. - Failed lookups are reported in `OUTPUT.errors` instead of being silently ignored. # Actor input Schema ## `seedKeywords` (type: `array`): Base terms, products, categories, styles, seasons, events, or topics to research. ## `expansionMode` (type: `string`): Choose exact seed lookups, alphabet long-tail expansion, or inspiration/research phrase expansion. ## `maxQueries` (type: `integer`): Maximum number of generated autocomplete lookups for this run. ## `maxSuggestionsPerQuery` (type: `integer`): Maximum number of suggestions saved for each autocomplete lookup. ## `deduplicate` (type: `boolean`): Keep only the first occurrence of each suggestion across the run. ## Actor input object example ```json { "seedKeywords": [ "wedding decor", "nail designs" ], "expansionMode": "none", "maxQueries": 50, "maxSuggestionsPerQuery": 20, "deduplicate": true } ``` # 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("dromb/pinterest-keyword-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("dromb/pinterest-keyword-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 dromb/pinterest-keyword-autocomplete-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dromb/pinterest-keyword-autocomplete-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pinterest Keyword Autocomplete Scraper", "description": "Discover live Pinterest autocomplete keyword ideas for Pinterest SEO, content planning, product research, and trend discovery.", "version": "0.1", "x-build-id": "BsvdFXhttgdshG70z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dromb~pinterest-keyword-autocomplete-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dromb-pinterest-keyword-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dromb~pinterest-keyword-autocomplete-scraper/runs": { "post": { "operationId": "runs-sync-dromb-pinterest-keyword-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dromb~pinterest-keyword-autocomplete-scraper/run-sync": { "post": { "operationId": "run-sync-dromb-pinterest-keyword-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "seedKeywords": { "title": "Seed Keywords", "type": "array", "description": "Base terms, products, categories, styles, seasons, events, or topics to research.", "items": { "type": "string" }, "default": [ "wedding decor", "nail designs" ] }, "expansionMode": { "title": "Expansion Mode", "enum": [ "none", "alphabet", "questions" ], "type": "string", "description": "Choose exact seed lookups, alphabet long-tail expansion, or inspiration/research phrase expansion.", "default": "none" }, "maxQueries": { "title": "Max Queries", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of generated autocomplete lookups for this run.", "default": 50 }, "maxSuggestionsPerQuery": { "title": "Max Suggestions Per Query", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of suggestions saved for each autocomplete lookup.", "default": 20 }, "deduplicate": { "title": "Deduplicate Suggestions", "type": "boolean", "description": "Keep only the first occurrence of each suggestion across the run.", "default": true } } }, "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 } } } } } } } } } } ```