# Otto.de MCP Server (`axlymxp/otto-mcp`) Actor MCP server for otto.de — search products, browse categories, fetch full details and reviews live from Claude, Cursor and other AI agents. - **URL**: https://apify.com/axlymxp/otto-mcp.md - **Developed by:** [axly](https://apify.com/axlymxp) (community) - **Categories:** AI, E-commerce, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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 ## Otto.de MCP Server Query **otto.de** products, prices, and reviews live from **Claude, Cursor, n8n,** or any MCP-compatible AI agent — without writing a single line of scraping code. ### What it does Runs as an MCP (Model Context Protocol) server on the Apify platform. Connect it once; your AI agent can then search otto.de products, browse categories, fetch full product details (GTIN, description, all images), and retrieve customer reviews — all in real time, as live tool calls. ### Who uses this - **AI/agent developers** — embed live otto.de product lookup into RAG pipelines, shopping assistants, and price-monitoring agents - **Claude / Cursor users** — ask about otto.de products directly in your IDE or chat without leaving your workflow - **PropTech & SaaS developers** — compose otto.de data into larger LLM workflows, n8n automations, or custom agents ### MCP Tools | Tool | Description | |---|---| | `search_otto_products` | Keyword search — returns name, price, brand, rating, availability, image | | `browse_otto_category` | Browse a category path (e.g. `/moebel/sofas/`) | | `get_otto_product` | Full product detail for any URL — GTIN/EAN, SKU, description, all images, return policy | | `get_otto_reviews` | Customer reviews for a product ID | | `get_otto_suggestions` | Search autocomplete suggestions | #### Example tool call ```` search\_otto\_products(keywords="ecksofa grau", limit=5, sort="price\_asc") ```` Returns: ```json { "query": "ecksofa grau", "count": 5, "results": [ { "name": "VASAGLE Ecksofa L-Form 244 cm", "price": "339.99", "currency": "EUR", "brand": "VASAGLE", "availability": "InStock", "rating": "4", "url": "https://www.otto.de/p/vasagle-..." } ] } ```` ### Connect to Claude 1. Deploy the actor on Apify in **Standby mode**. 2. Copy the Standby URL (format: `https://.apify.actor`). 3. In Claude's MCP settings, add a remote server: ``` URL: https://.apify.actor/mcp Transport: Streamable HTTP ``` 4. Ask Claude: *"Search otto.de for a grey corner sofa under €400"* — it calls the tool live. ### Connect to Cursor / VS Code Add to your MCP config: ```json { "mcpServers": { "otto-de": { "url": "https://.apify.actor/mcp", "transport": "streamable-http" } } } ``` ### One-shot scrape mode Provide `keywords` in the input to run a classic one-shot scrape and push results to a dataset instead of starting the MCP server. Useful for scheduled batch jobs. ```json { "keywords": "laptop 16 zoll", "limit": 100 } ``` ### Input parameters (one-shot mode) | Parameter | Type | Default | Description | |---|---|---|---| | `keywords` | string | — | Search query for one-shot run | | `limit` | integer | 50 | Max results in one-shot mode | ### Scheduling & integrations Schedule a daily one-shot run to keep a dataset fresh. Use webhooks to trigger downstream workflows in Make, Zapier, or n8n when the run completes. ### FAQ **How is this different from the otto-scraper?** This actor is an always-on MCP server — your AI agent calls it live, like an API. The [otto-scraper](https://apify.com/axlymxp/otto-scraper) is a batch tool that runs once and saves to a dataset. **Does `get_otto_product` handle Kasada anti-bot?** Yes — it uses the Android app cookie trick to bypass Kasada on product detail pages without a browser. **Can I query any otto.de category?** Yes — pass any category path like `/computer/laptops/` or `/sport-freizeit/fahrraeder/` to `browse_otto_category`. **What is the per-call cost?** Each MCP tool call is charged as one Pay-Per-Event unit (see the Store listing for current prices). There is no monthly fee. # Actor input Schema ## `keywords` (type: `string`): Optional: run a one-shot keyword search and push results to the dataset instead of starting the MCP server. Leave empty to run as MCP server. ## `limit` (type: `integer`): Max results to return in one-shot mode. Ignored in MCP server mode. ## Actor input object example ```json { "keywords": "ecksofa", "limit": 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/otto-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("axlymxp/otto-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 axlymxp/otto-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=axlymxp/otto-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Otto.de MCP Server", "description": "MCP server for otto.de — search products, browse categories, fetch full details and reviews live from Claude, Cursor and other AI agents.", "version": "1.0", "x-build-id": "Xj7Vf6ouTupbkaKvl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/axlymxp~otto-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-axlymxp-otto-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/axlymxp~otto-mcp/runs": { "post": { "operationId": "runs-sync-axlymxp-otto-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/axlymxp~otto-mcp/run-sync": { "post": { "operationId": "run-sync-axlymxp-otto-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keywords": { "title": "Search keywords (one-shot mode)", "type": "string", "description": "Optional: run a one-shot keyword search and push results to the dataset instead of starting the MCP server. Leave empty to run as MCP server." }, "limit": { "title": "Max results (one-shot mode)", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Max results to return in one-shot mode. Ignored in MCP server mode.", "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 } } } } } } } } } } ```