# BasketWatch MCP Server — Irish Grocery Data for AI Agents (`hawkish_gym/basketwatch-mcp`) Actor MCP server for live Irish supermarket data: shelf and unit prices, promotions, loyalty-card prices and weekly price changes across Aldi, Tesco, SuperValu and Dunnes. Connect Claude, Cursor or any MCP client to ask grocery questions directly. Standby mode; billed per tool request. - **URL**: https://apify.com/hawkish\_gym/basketwatch-mcp.md - **Developed by:** [BasketWatch](https://apify.com/hawkish_gym) (community) - **Categories:** Agents, AI, E-commerce - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 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 ## BasketWatch MCP Server — Irish Grocery Data for AI Agents A hosted [Model Context Protocol](https://modelcontextprotocol.io/) server that gives your AI agent live **Irish supermarket data** — shelf prices, unit prices, promotions, loyalty-card prices and week-over-week price changes across **Aldi, Tesco, SuperValu and Dunnes Stores**. > ~47,000 Irish supermarket SKUs, ~16,000 on promotion at any time, refreshed > nightly across all four retailers. Now queryable directly from Claude, > Cursor, Windsurf or any MCP client. This Actor runs in **Standby mode**: it's always-on at a stable URL and you connect to it over **Streamable HTTP**. You're billed **per tool request** — no subscription, no API key to manage. Apify handles auth and billing. ### What your agent can do Eight grocery-aware tools: | Tool | What it does | |---|---| | `status` | Cross-retailer freshness snapshot — SKU counts, last-scrape dates, run status | | `search_products` | Find products by name in one or all stores | | `compare_price_across_stores` | One-call comparison across all 4 supermarkets | | `get_promotions` | Products currently on offer at a given store | | `recent_price_changes` | Week-over-week price movements | | `newly_added_products` | New listings in a configurable lookback window | | `removed_products` | Delistings / range cuts | | `list_products` | Paginated catalogue dump for walking the full assortment | Example questions: - *"Where can I buy Heinz Baked Beans cheapest this week?"* - *"Build me a €40 weekly grocery list across the 4 supermarkets."* - *"Which products got cheaper at Tesco this week?"* - *"What promotions are running on chocolate at Dunnes right now?"* ### Connect your MCP client The server lives at your Standby URL: ```` https://--basketwatch-mcp.apify.actor/mcp ```` Authenticate with your Apify token via the `Authorization: Bearer ` header (or the `?token=` query param where your client requires it). #### Claude Desktop / Cursor / Windsurf ```json { "mcpServers": { "basketwatch": { "url": "https://--basketwatch-mcp.apify.actor/mcp", "headers": { "Authorization": "Bearer " } } } } ```` Restart the client; "basketwatch" appears as an available MCP server. Ask it *"What's the cheapest 1L of milk in Ireland?"* and it'll use the tools automatically. ### Pricing Pay-per-event: **one charge per successful tool request**. Failed calls (network/API errors) are not billed. See the **Pricing** tab on this Store listing for the current per-request rate and any Apify subscription-tier discounts. Need bulk exports or higher cadence (daily / real-time) instead of agent-style lookups? The full BasketWatch data API is sold separately — email **info@basketwatchireland.com**. ### Data freshness - All four retailers refresh **every night**. - Rows carry a `scrape_date` so the agent knows when each price was captured. ### Not affiliated BasketWatch is not affiliated with Aldi Ireland, Tesco Ireland, Musgrave SuperValu or Dunnes Stores. Data is collected from publicly available sources for lawful market research and price comparison. # Actor input Schema ## Actor input object example ```json {} ``` # 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("hawkish_gym/basketwatch-mcp").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("hawkish_gym/basketwatch-mcp").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 hawkish_gym/basketwatch-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=hawkish_gym/basketwatch-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "BasketWatch MCP Server — Irish Grocery Data for AI Agents", "description": "MCP server for live Irish supermarket data: shelf and unit prices, promotions, loyalty-card prices and weekly price changes across Aldi, Tesco, SuperValu and Dunnes. Connect Claude, Cursor or any MCP client to ask grocery questions directly. Standby mode; billed per tool request.", "version": "0.1", "x-build-id": "9MQYm5tUj5wz2raWh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/hawkish_gym~basketwatch-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-hawkish_gym-basketwatch-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/hawkish_gym~basketwatch-mcp/runs": { "post": { "operationId": "runs-sync-hawkish_gym-basketwatch-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/hawkish_gym~basketwatch-mcp/run-sync": { "post": { "operationId": "run-sync-hawkish_gym-basketwatch-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```