# Zepto MCP Server (`axlymxp/zepto-mcp-server`) Actor MCP server + scraper for Zepto (India quick-commerce grocery). Search products, get product detail, autocomplete, and resolve stores by delivery location — live from Claude, ChatGPT, Cursor and other AI agents — or run it as a classic Zepto scraper. - **URL**: https://apify.com/axlymxp/zepto-mcp-server.md - **Developed by:** [axly](https://apify.com/axlymxp) (community) - **Categories:** AI, Agents, E-commerce - **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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Zepto MCP Server Give your AI assistant live access to **Zepto** — India's quick-commerce grocery platform. This actor runs as a **Model Context Protocol (MCP) server** so agents like **Claude, ChatGPT, Cursor, and n8n** can search products, pull prices and stock, and resolve delivery stores on demand. It also works as a **classic scraper** when run normally. Built on Zepto's mobile API (header-only, no login, no unblocking subscription), so keyword search returns the full priced product grid — reliably. ### MCP tools | Tool | What it does | | ----------------------- | ----------------------------------------------------------------------------------------------------------------- | | `search_zepto_products` | Search a keyword and return priced product rows for a location (name, brand, MRP, price, discount, stock, image). | | `get_zepto_product` | Full detail for a product-variant id at a delivery location. | | `zepto_autocomplete` | Search-box autocomplete suggestions for a partial query. | | `resolve_zepto_store` | Resolve the serving store (and serviceability) for a lat/lng or city. | Every tool is **location-scoped** — pass an Indian city name (e.g. `Mumbai`) or explicit `latitude`/`longitude`, since Zepto prices and stock vary by store. ### Connect an AI assistant The actor runs in **Standby mode** and speaks MCP over **Streamable HTTP** at: ```` https:///mcp ```` Add it to an MCP-capable client (Claude Desktop, Cursor, etc.) as a Streamable HTTP server, using your Apify API token as a bearer token. Then ask naturally: > "What's the cheapest milk on Zepto in Mumbai right now?" > "Compare Amul butter prices between Delhi and Bengaluru." ### Use cases - **Shopping & price assistants** — let an agent answer live grocery price/stock questions. - **Research & monitoring agents** — automate assortment and discount checks across cities. - **Workflow automation (n8n, Make)** — wire Zepto lookups into agentic pipelines. ### Also runs as a scraper Run the actor normally (not standby) with `searchQueries` + a `location` to push normalized product rows to a dataset — handy for scheduled jobs and quick tests. #### Example (normal run) input ```json { "searchQueries": ["milk", "amul butter"], "location": "Mumbai", "maxPagesPerQuery": 1, "maxItems": 100 } ```` ### Output fields Product rows include `name`, `brand`, `pack_size`, `mrp`, `selling_price`, `super_saver_price`, `discount_percent`, `in_stock`, `category`, `subcategory`, `image`, `store_id`, `location_label`, and `scraped_at`. See the dataset schema for the full list. ### FAQ **Do I need an API key?** You connect with your Apify token as the bearer token for the MCP endpoint. No Zepto login is required. **Which locations work?** Any serviceable point in India — a city name or exact coordinates. **How fresh is the data?** Every tool call fetches live prices and stock. **Is this legal?** You are responsible for your use of the data. Query publicly available information and comply with Zepto's terms and applicable law. # Actor input Schema ## `searchQueries` (type: `array`): Keywords to search on Zepto (normal run mode only). ## `location` (type: `string`): A known Indian city name (e.g. 'Mumbai') or 'lat,lng'. Zepto prices are store-scoped. ## `latitude` (type: `number`): Delivery latitude (overrides 'location' when set with longitude). ## `longitude` (type: `number`): Delivery longitude. ## `maxPagesPerQuery` (type: `integer`): Search pages per keyword (~30 products/page). ## `maxItems` (type: `integer`): Global cap on rows in normal run mode. ## Actor input object example ```json { "searchQueries": [ "milk", "amul butter" ], "location": "Mumbai", "maxPagesPerQuery": 1, "maxItems": 100 } ``` # 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 = { "searchQueries": [ "milk" ], "location": "Mumbai" }; // Run the Actor and wait for it to finish const run = await client.actor("axlymxp/zepto-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 = { "searchQueries": ["milk"], "location": "Mumbai", } # Run the Actor and wait for it to finish run = client.actor("axlymxp/zepto-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 '{ "searchQueries": [ "milk" ], "location": "Mumbai" }' | apify call axlymxp/zepto-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=axlymxp/zepto-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Zepto MCP Server", "description": "MCP server + scraper for Zepto (India quick-commerce grocery). Search products, get product detail, autocomplete, and resolve stores by delivery location — live from Claude, ChatGPT, Cursor and other AI agents — or run it as a classic Zepto scraper.", "version": "1.0", "x-build-id": "qI0AG2waDwc8I9m3s" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/axlymxp~zepto-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-axlymxp-zepto-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~zepto-mcp-server/runs": { "post": { "operationId": "runs-sync-axlymxp-zepto-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~zepto-mcp-server/run-sync": { "post": { "operationId": "run-sync-axlymxp-zepto-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": "Keywords to search on Zepto (normal run mode only).", "default": [], "items": { "type": "string" } }, "location": { "title": "Delivery location", "type": "string", "description": "A known Indian city name (e.g. 'Mumbai') or 'lat,lng'. Zepto prices are store-scoped.", "default": "Mumbai" }, "latitude": { "title": "Latitude", "type": "number", "description": "Delivery latitude (overrides 'location' when set with longitude)." }, "longitude": { "title": "Longitude", "type": "number", "description": "Delivery longitude." }, "maxPagesPerQuery": { "title": "Max search pages per query", "minimum": 1, "maximum": 50, "type": "integer", "description": "Search pages per keyword (~30 products/page).", "default": 1 }, "maxItems": { "title": "Max items (total)", "minimum": 1, "type": "integer", "description": "Global cap on rows in normal run mode.", "default": 100 } } }, "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 } } } } } } } } } } ```