# Swiggy MCP Server (`axlymxp/swiggy-mcp-server`) Actor Query Swiggy India from your AI assistant. This MCP server lets Claude, ChatGPT, Cursor and other agents search restaurants, fetch full menus with prices, search dishes and geocode locations live. Also runs as a classic scraper. Pay only per tool call. - **URL**: https://apify.com/axlymxp/swiggy-mcp-server.md - **Developed by:** [axly](https://apify.com/axlymxp) (community) - **Categories:** AI, Developer tools, E-commerce - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 dataset items 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 ## Swiggy MCP Server Give your **AI assistant live access to Swiggy** (India's largest food‑delivery platform). This actor runs a **Model Context Protocol (MCP) server** so agents like **Claude, ChatGPT, Cursor, and n8n** can search restaurants, fetch full menus with prices, search dishes, and geocode locations on demand — no scraping code required. It also runs as a **classic scraper** (input → dataset) when you just want a batch of rows. ### Who it's for - **AI‑assistant & agent builders** who need live Indian food‑delivery data in their tools. - **Analysts** doing ad‑hoc restaurant/menu lookups from Claude or Cursor. - **SaaS teams** embedding Swiggy data into LLM workflows. ### MCP tools | Tool | Description | | -------------------- | ---------------------------------------------------------------------------------------------------------- | | `search_restaurants` | Search or list restaurants near an Indian location — rating, cuisines, cost for two, delivery ETA, offers. | | `get_menu` | Fetch a restaurant's full menu (categories → items with prices, veg flags, availability) by ID or URL. | | `search_dishes` | Find restaurants matching a dish keyword near a location. | | `geocode` | Resolve an Indian place/address to coordinates. | ### Connect your AI assistant 1. Start the actor in **Standby mode** (default) — it serves MCP over Streamable HTTP. 2. Point your MCP client at the actor's standby URL + `/mcp`: ```json { "mcpServers": { "swiggy": { "url": "https:///mcp" } } } ```` 3. Ask your assistant things like *"Find top‑rated biryani places in Koramangala and show the menu for the first one."* ### Example tool call ```json // search_restaurants { "location": "Bangalore", "query": "pizza", "limit": 10 } ``` ```json // get_menu { "restaurant": "12808", "location": "Bangalore" } ``` ### Classic (dataset) mode Run it normally with an input to push restaurant rows to a dataset: ```json { "location": "Bangalore", "searchQuery": "biryani", "maxRestaurants": 20 } ``` ### Use cases - **Conversational food discovery** inside an AI product. - **Agentic price/menu lookups** for a specific restaurant on demand. - **Location‑aware restaurant search** as a tool in a larger agent workflow. ### FAQ **Which countries?** India only — Swiggy's coverage. **Do I need an API key?** No. The actor handles Swiggy's anti‑bot challenge automatically. **What transport does the MCP server use?** Streamable HTTP at `/mcp`. **Is the data live?** Yes — every tool call queries Swiggy in real time. **Is this legal?** It accesses publicly available data; use the output in line with Swiggy's terms and applicable laws. # Actor input Schema ## `location` (type: `string`): Indian city or area to browse in classic (non-MCP) run mode, e.g. "Bangalore". In MCP mode, the AI agent passes this per tool call. ## `searchQuery` (type: `string`): Dish or restaurant keyword for classic run mode (e.g. "biryani"). ## `lat` (type: `number`): Exact latitude (overrides location). ## `lng` (type: `number`): Exact longitude. ## `maxRestaurants` (type: `integer`): Max restaurants to return in classic run mode. ## Actor input object example ```json { "location": "Bangalore", "maxRestaurants": 20 } ``` # 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 = { "location": "Bangalore" }; // Run the Actor and wait for it to finish const run = await client.actor("axlymxp/swiggy-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 = { "location": "Bangalore" } # Run the Actor and wait for it to finish run = client.actor("axlymxp/swiggy-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 '{ "location": "Bangalore" }' | apify call axlymxp/swiggy-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=axlymxp/swiggy-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Swiggy MCP Server", "description": "Query Swiggy India from your AI assistant. This MCP server lets Claude, ChatGPT, Cursor and other agents search restaurants, fetch full menus with prices, search dishes and geocode locations live. Also runs as a classic scraper. Pay only per tool call.", "version": "1.0", "x-build-id": "eN8IZb1BLXJLAhWsW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/axlymxp~swiggy-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-axlymxp-swiggy-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/axlymxp~swiggy-mcp-server/runs": { "post": { "operationId": "runs-sync-axlymxp-swiggy-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/axlymxp~swiggy-mcp-server/run-sync": { "post": { "operationId": "run-sync-axlymxp-swiggy-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "location": { "title": "Location (city or area)", "type": "string", "description": "Indian city or area to browse in classic (non-MCP) run mode, e.g. \"Bangalore\". In MCP mode, the AI agent passes this per tool call." }, "searchQuery": { "title": "Search query (optional)", "type": "string", "description": "Dish or restaurant keyword for classic run mode (e.g. \"biryani\")." }, "lat": { "title": "Latitude (optional)", "type": "number", "description": "Exact latitude (overrides location)." }, "lng": { "title": "Longitude (optional)", "type": "number", "description": "Exact longitude." }, "maxRestaurants": { "title": "Max restaurants", "minimum": 1, "type": "integer", "description": "Max restaurants to return in classic run mode.", "default": 20 } } }, "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 } } } } } } } } } } ```