# Klook MCP Server (`axlymxp/klook-mcp-server`) Actor MCP server + scraper for Klook. Search activities, browse cities, resolve destinations and pull categories live from Claude, ChatGPT, Cursor and other AI agents — or run it as a classic Klook scraper. Prices, ratings, reviews, bookings and images as structured data. - **URL**: https://apify.com/axlymxp/klook-mcp-server.md - **Developed by:** [axly](https://apify.com/axlymxp) (community) - **Categories:** Travel, AI, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage 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 ## Klook MCP Server Give your AI assistant **live access to Klook** — the travel-experiences marketplace. This Actor runs as a **Model Context Protocol (MCP) server** so Claude, ChatGPT, Cursor, n8n and custom agents can search activities, browse cities, resolve destinations and pull categories on demand. It also works as a **classic one-shot scraper** if you just want a dataset. Backed by Klook's **mobile API** (with in-Python request signing and DataDome handling) for reliable, structured results. ### Who it's for - **AI-agent & app builders** — add real Klook activity search to an assistant or a RAG/travel-planning workflow without touching anti-bot or auth. - **Travel-tech developers** — a hosted MCP endpoint for activity data; no scraping code to maintain. - **Analysts & researchers** — interactively explore activities, prices and demand by city from a chat interface. ### MCP tools | Tool | What it does | | --------------------------- | ------------------------------------------------------------------------------------- | | `search_klook_activities` | Keyword search → activities with price, rating, reviews, bookings, city, GPS, image. | | `get_klook_city_activities` | Browse a city's full activity catalog by numeric city ID. | | `search_klook_suggestions` | Autocomplete cities (with IDs) and activities for a term — resolve destination names. | | `get_klook_city_info` | City overview: travel tips, weather, hot activities, nearby cities. | | `get_klook_activity_images` | Image gallery for a specific activity. | | `get_klook_categories` | Klook's top-level activity category / vertical tree. | ### Connect it to an AI assistant 1. Start this Actor in **Standby mode** (it launches an MCP server). 2. Point your MCP client at the server URL + `/mcp` (Streamable HTTP transport). Example MCP client config: ```json { "mcpServers": { "klook": { "url": "https:///mcp" } } } ```` Then ask your assistant things like *"Find the top-rated Universal Studios Japan tickets on Klook"* or *"What are the most-booked activities in Tokyo?"*. ### Classic scraper mode Prefer a dataset? Run it normally with input: ```json { "searchQueries": ["universal studios japan"], "cityIds": ["28"], "sortBy": "most_reviewed", "maxItems": 50 } ``` Each activity is pushed as a structured row (same fields as the tool output): `activity_id`, `title`, `activity_url`, `rating`, `review_count`, `booked_count`, `sell_price`, `price_from`/`price_to`, `city_name`, `country_name`, `latitude`, `longitude`, `image_url`, `scraped_at`, and more. ### Scheduling & integrations Schedule classic-mode runs and pipe results to Google Sheets, Make, Zapier, S3 or your API via Apify webhooks and the dataset API (JSON, CSV, Excel). ### FAQ **What is MCP?** The Model Context Protocol — an open standard that lets AI assistants call external tools. This Actor is a hosted MCP server for Klook. **Where does the data come from?** Klook's official mobile backend API — the same source the Klook app uses. **How fresh is it?** Live at call time. **Do I need the sibling scraper?** No — this Actor also scrapes to a dataset. Use the **Klook Activities & Experiences Scraper** if you only want bulk dataset runs without MCP. **Is it legal?** You are responsible for your use of the data; it collects publicly available catalog information. Respect Klook's terms and applicable laws. # Actor input Schema ## `searchQueries` (type: `array`): Optional (classic-scrape mode). Keywords to search Klook activities for. ## `cityIds` (type: `array`): Optional (classic-scrape mode). Klook numeric city IDs to browse (e.g. 28 = Tokyo). ## `sortBy` (type: `string`): Ordering of returned activities in classic-scrape mode. ## `maxItems` (type: `integer`): Classic-scrape mode: max activities per query/city. ## Actor input object example ```json { "searchQueries": [ "universal studios japan" ], "cityIds": [ "28" ], "sortBy": "default", "maxItems": 50 } ``` # 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("axlymxp/klook-mcp-server").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("axlymxp/klook-mcp-server").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 axlymxp/klook-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=axlymxp/klook-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Klook MCP Server", "description": "MCP server + scraper for Klook. Search activities, browse cities, resolve destinations and pull categories live from Claude, ChatGPT, Cursor and other AI agents — or run it as a classic Klook scraper. Prices, ratings, reviews, bookings and images as structured data.", "version": "1.0", "x-build-id": "1qv8CORlNx2slt55r" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/axlymxp~klook-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-axlymxp-klook-mcp-server", "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/axlymxp~klook-mcp-server/runs": { "post": { "operationId": "runs-sync-axlymxp-klook-mcp-server", "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/axlymxp~klook-mcp-server/run-sync": { "post": { "operationId": "run-sync-axlymxp-klook-mcp-server", "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": { "searchQueries": { "title": "Search queries", "type": "array", "description": "Optional (classic-scrape mode). Keywords to search Klook activities for.", "items": { "type": "string" } }, "cityIds": { "title": "City IDs", "type": "array", "description": "Optional (classic-scrape mode). Klook numeric city IDs to browse (e.g. 28 = Tokyo).", "items": { "type": "string" } }, "sortBy": { "title": "Sort by", "enum": [ "default", "most_reviewed", "price_low_high", "price_high_low", "rating" ], "type": "string", "description": "Ordering of returned activities in classic-scrape mode.", "default": "default" }, "maxItems": { "title": "Max activities (per source)", "minimum": 1, "type": "integer", "description": "Classic-scrape mode: max activities per query/city.", "default": 50 } } }, "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 } } } } } } } } } } ```