# Bing Autocomplete Keyword Suggestions Scraper (`dt_org/dreemteam-bing-autocomplete`) Actor Collect Bing autocomplete keyword suggestions for SEO research, content planning, and market analysis. - **URL**: https://apify.com/dt\_org/dreemteam-bing-autocomplete.md - **Developed by:** [DreamTeam](https://apify.com/dt_org) (community) - **Categories:** Automation, Developer tools, Other - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.30 / 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 ## Bing Autocomplete Keyword Suggestions Scraper Collect Bing autocomplete keyword suggestions from seed keywords and turn them into clean rows for SEO research, content planning, and market analysis. Use this Actor to discover long-tail keyword ideas, compare search intent, expand topic clusters, build content briefs, and enrich keyword research workflows with repeatable Bing suggestion data. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Microsoft, Bing, or their affiliates. ### What You Can Collect - Bing autocomplete suggestions for each lookup query. - The original seed keyword that generated each lookup. - The exact lookup query used for each suggestion. - Suggestion position within the returned autocomplete list. - Collection timestamp and source label for repeatable exports. ### Common Use Cases - Build SEO keyword lists from short seed terms. - Find long-tail content ideas around brands, products, and topics. - Expand paid search and marketplace research terms. - Compare autocomplete demand across multiple seed keywords. - Feed keyword suggestions into spreadsheets, BI tools, or enrichment pipelines. ### Input - `seedKeywords` - base terms, brands, products, categories, or topics to research. - `expansionMode` - choose how lookup queries are generated: - `none` uses each seed keyword exactly as entered. - `alphabet` appends `a` through `z` to find long-tail variants. - `questions` generates buyer and research-style query patterns. - `maxQueries` - maximum number of generated autocomplete lookups for the run. - `maxSuggestionsPerQuery` - maximum number of suggestions saved for each lookup. - `deduplicate` - keep only the first occurrence of each suggestion across the run. ### Example Input ```json { "seedKeywords": ["web scraping"], "expansionMode": "none", "maxQueries": 5, "maxSuggestionsPerQuery": 10, "deduplicate": true } ```` ### Alphabet Expansion Example ```json { "seedKeywords": ["email marketing"], "expansionMode": "alphabet", "maxQueries": 10, "maxSuggestionsPerQuery": 10, "deduplicate": true } ``` ### Output Each dataset row is one useful autocomplete suggestion: - `seedKeyword` - `query` - `position` - `suggestion` - `source` - `collectedAt` Example row: ```json { "seedKeyword": "web scraping", "query": "web scraping", "position": 1, "suggestion": "web scraping tools", "source": "bing", "collectedAt": "2026-07-04T00:00:00+00:00" } ``` The key-value store `OUTPUT` object contains run status, input count, output count, quota status, warnings, and errors. ### Result Counting Each autocomplete suggestion is written as one dataset row. Status summaries, empty results, errors, and quota-limit outcomes are reported in `OUTPUT` and do not create extra dataset rows. ### Limits Start with small inputs when testing a new keyword set. Autocomplete suggestions can vary by time, availability, and source behavior, so repeated runs may return slightly different suggestion lists. # Actor input Schema ## `seedKeywords` (type: `array`): Base terms, brands, products, categories, or topics to research. ## `expansionMode` (type: `string`): Choose exact seed lookups, alphabet long-tail expansion, or buyer/research question 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": [ "web scraping" ], "expansionMode": "none", "maxQueries": 50, "maxSuggestionsPerQuery": 20, "deduplicate": true } ``` # Actor output Schema ## `results` (type: `string`): Dataset rows. Each row is one autocomplete suggestion with seed and query context. ## `summary` (type: `string`): Structured run status, counts, quota status, warnings, and errors. # 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("dt_org/dreemteam-bing-autocomplete").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("dt_org/dreemteam-bing-autocomplete").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 dt_org/dreemteam-bing-autocomplete --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dt_org/dreemteam-bing-autocomplete", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Bing Autocomplete Keyword Suggestions Scraper", "description": "Collect Bing autocomplete keyword suggestions for SEO research, content planning, and market analysis.", "version": "0.1", "x-build-id": "ZGbbyPJYt9BWAecSn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dt_org~dreemteam-bing-autocomplete/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dt_org-dreemteam-bing-autocomplete", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dt_org~dreemteam-bing-autocomplete/runs": { "post": { "operationId": "runs-sync-dt_org-dreemteam-bing-autocomplete", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dt_org~dreemteam-bing-autocomplete/run-sync": { "post": { "operationId": "run-sync-dt_org-dreemteam-bing-autocomplete", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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, brands, products, categories, or topics to research.", "items": { "type": "string" }, "default": [ "web scraping" ] }, "expansionMode": { "title": "Expansion Mode", "enum": [ "none", "alphabet", "questions" ], "type": "string", "description": "Choose exact seed lookups, alphabet long-tail expansion, or buyer/research question 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 } } } } } } } } } } ```