# German Real Estate MCP (`rigelbytes/german-realestate-mcp`) Actor MCP server for German real estate β€” 14 Rigel Bytes scrapers (ImmoScout24, Immowelt, Kleinanzeigen, RE/MAX, CBRE, Colliers, JLL, and more). Connect from Cursor, Claude, or VS Code. - **URL**: https://apify.com/rigelbytes/german-realestate-mcp.md - **Developed by:** [Rigel Bytes](https://apify.com/rigelbytes) (community) - **Categories:** MCP servers, Integrations, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 tool calls 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 ### What does German Real Estate MCP do? **German Real Estate MCP** is a [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server by [Rigel Bytes](https://apify.com/rigelbytes) that connects AI assistants β€” Cursor, Claude Desktop, VS Code Copilot, and others β€” to **14 curated German property scrapers** through a single endpoint. Ask your AI agent for listings from **ImmoScout24**, **Immowelt**, **Kleinanzeigen**, **RE/MAX**, **CBRE**, **Colliers**, **JLL**, and more β€” without writing API code or switching between tools. The server runs on Apify in **standby mode** and proxies each MCP tool call to the matching Rigel Bytes scraper on **your** Apify account. You pay **$0.05 per MCP tool call** plus the normal usage cost of each underlying scraper. **MCP endpoint:** `https://rigelbytes--german-realestate-mcp.apify.actor/mcp` --- ### What can German Real Estate MCP do for you? - 🏠 **Search residential rentals and sales** across Immowelt, ImmoScout24, Kleinanzeigen, and major broker networks - 🏒 **Extract commercial property data** from CBRE, Colliers, JLL, Savills, BNP Paribas RE, and Aengevelt - πŸ“Š **Power market research and investment analysis** with structured JSON from 14 German real estate sources - πŸ€– **Let AI agents pick the right scraper** β€” one MCP server, automatic routing to the correct portal or broker - ⚑ **Integrate with Cursor, Claude, or VS Code** β€” connect once, query in natural language - πŸ’° **Control costs** β€” set `maxItems` on each scraper call; preview results before fetching full datasets --- ### Included MCP tools | Tool | Source | Best for | |------|--------|----------| | `immobilienscout24-scraper` | [ImmoScout24](https://www.immobilienscout24.de) | Germany's largest portal β€” pass a search URL | | `immowelt-property-scraper` | [Immowelt](https://www.immowelt.de) | City + price/room filters | | `kleinanzeigen-scraper` | [Kleinanzeigen](https://www.kleinanzeigen.de) | Classified / private listings via keywords | | `remax-scraper` | [RE/MAX Germany](https://www.remax.de) | Listings by Bundesland | | `dahler-scraper` | [DAHLER Immobilien](https://www.dahler.de) | DAHLER network buy/rent listings | | `grandcityproperty` | [GrandCityProperty](https://www.grandcityproperty.com) | Large rental portfolio | | `wuestenrot-immobilien` | [WΓΌstenrot Immobilien](https://www.wuestenrot-immobilien.de) | WΓΌstenrot broker listings | | `cbre-scraper` | [CBRE Germany](https://www.cbre.de) | Commercial office & industrial | | `colliers-scraper` | [Colliers](https://www.colliers.de) | Office, logistics, retail | | `jll-scraper` | [JLL Germany](https://property.jll.de) | Commercial & investment | | `savills-scraper` | [Savills](https://www.savills.de) | Residential & commercial by city | | `bnppre-scraper` | [BNP Paribas RE](https://www.bnppre.de) | Commercial property | | `aengevelt-scraper` | [Aengevelt](https://www.aengevelt.com) | Broker network listings | | `grossmann-berger-scraper` | [Grossmann & Berger](https://www.grossmann-berger.de) | Hamburg-based broker | **Helper tools:** `list_scrapers` (routing guide), `get_actor_output` (full dataset after a run) --- ### Why use this MCP server? - πŸ‡©πŸ‡ͺ **Built for Germany** β€” portals, brokers, and commercial firms in one bundle - 🎯 **Agent-ready routing** β€” each tool includes when-to-use guidance so AI picks the right source - πŸ”’ **Runs on your Apify account** β€” your token, your data, your billing - πŸ“¦ **One connection** β€” no need to configure 14 separate actors in your MCP client - πŸ› οΈ **Production standby server** β€” always-on endpoint, no cold-start batch jobs - πŸ“ˆ **Scalable** β€” schedule monitoring, compare markets, feed CRMs via Apify datasets --- ### Pricing | Charge | Cost | Who receives it | |--------|------|-----------------| | MCP tool call | **$0.05** per invocation | Rigel Bytes (this Actor) | | Underlying scraper runs | Per scraper pricing on [Rigel Bytes Store](https://apify.com/rigelbytes) | Rigel Bytes + Apify platform usage | Example: one Immowelt search returning 100 listings might cost **$0.05** (MCP call) + scraper usage (e.g. **$1.00** per 1,000 listings on Immowelt) + Apify compute. Apify plan discounts apply to platform usage. Check each scraper's Store page for current per-unit rates. --- ### How to use German Real Estate MCP #### Step 1 β€” Create an Apify account Sign up at [Apify Console](https://console.apify.com). Free tier available. #### Step 2 β€” Get your API token Open [API & Integrations](https://console.apify.com/settings/integrations) and copy your API token. #### Step 3 β€” Add MCP config to your AI client **Cursor** β€” create or edit `.cursor/mcp.json`: ```json { "mcpServers": { "rigel-german-real-estate": { "url": "https://rigelbytes--german-realestate-mcp.apify.actor/mcp", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ```` Replace `YOUR_APIFY_TOKEN` with your token from Step 2. **VS Code** β€” Command Palette β†’ **MCP: Open User Configuration** β†’ paste the same JSON. **Claude Desktop** β€” add a custom connector with the URL above and `Authorization: Bearer YOUR_APIFY_TOKEN` header. Or copy the ready-made config from `mcp-deployed.json` in this repository. #### Step 4 β€” Restart your AI client Reload Cursor / VS Code / Claude so the MCP server connects. #### Step 5 β€” Ask in natural language Examples: - *"Find rental apartments in Berlin under €1,500 on Immowelt"* - *"Scrape Kleinanzeigen for Wohnung mieten MΓΌnchen, max 25 listings"* - *"Get CBRE office listings in Frankfurt, max 20"* - *"List all scrapers in the German Real Estate MCP bundle"* The agent selects the right tool, runs the scraper on your account, and returns structured JSON. Use `get_actor_output` with the `datasetId` from the response for the full dataset. *** ### Alternative: Apify hosted MCP (no standby Actor) You can also connect via Apify's global MCP server with the same 14 scrapers pre-selected: ``` https://mcp.apify.com?tools=rigelbytes/immobilienscout24-scraper,rigelbytes/immowelt-property-scraper,rigelbytes/kleinanzeigen-scraper,rigelbytes/remax-scraper,rigelbytes/dahler-scraper,rigelbytes/grandcityproperty,rigelbytes/wuestenrot-immobilien,rigelbytes/cbre-scraper,rigelbytes/colliers-scraper,rigelbytes/jll-scraper,rigelbytes/savills-scraper,rigelbytes/bnppre-scraper,rigelbytes/aengevelt-scraper,rigelbytes/grossmann-berger-scraper,search-actors,fetch-actor-details,get-actor-output,search-apify-docs,fetch-apify-docs&telemetry-enabled=false ``` OAuth login is supported on [mcp.apify.com](https://mcp.apify.com) β€” no token in config required. *** ### Example tool call **Immowelt β€” rentals in Berlin:** ```json { "location": "Berlin", "pages": 2, "priceMin": 500, "priceMax": 1500 } ``` **Kleinanzeigen β€” keyword search:** ```json { "searchQueries": ["wohnung mieten berlin mitte"], "maxItems": 25 } ``` Always set `maxItems` or limit pages to control cost. *** ### Output Each scraper returns property listings as JSON in your Apify dataset. A preview (up to 25 items) is returned immediately. Call `get_actor_output` with the `datasetId` for the complete result. You can download datasets from Apify Console in **JSON, CSV, Excel, or HTML**. *** ### Tips and advanced options - Call `list_scrapers` first if the agent is unsure which portal to use - ImmoScout24 requires a **search results URL** β€” build the search on immobilienscout24.de, then pass the URL as `link` - Kleinanzeigen supports **semantic `searchQueries`** β€” other tools mostly need URLs or structured filters - For large runs, use `get_actor_output` with `limit` and `offset` to paginate - Test your setup in the [Apify Tester MCP Client](https://apify.com/jiri.spilka/tester-mcp-client) *** ### FAQ, disclaimers, and support **Do I need my own Apify account?**\ Yes. This MCP server runs scrapers on your account using your API token. **Is web scraping legal?**\ Scrape only data you are permitted to access. Respect website Terms of Service, robots.txt, and applicable laws (including GDPR for personal data). This tool is for legitimate business research. **Why do I pay twice?**\ The $0.05 MCP fee covers the gateway service. Scraper usage is billed separately per the underlying actor's pricing. **Can I use this without Cursor?**\ Any MCP-compatible client works β€” VS Code Copilot, Claude Desktop, custom agents via Streamable HTTP. **Need help or a custom integration?** - Email: - Book a call: [cal.com/faizanali/appointments](https://cal.com/faizanali/appointments) - More Rigel Bytes actors: [apify.com/rigelbytes](https://apify.com/rigelbytes) *** ### About Rigel Bytes Rigel Bytes specializes in web scraping, automation, and data analytics. We help businesses extract and leverage valuable data for informed decision-making. #### πŸš€ Other real estate tools by Rigel Bytes Browse our full catalog at [apify.com/rigelbytes](https://apify.com/rigelbytes?search=real+estate) β€” ImmoScout24, Immowelt, Kleinanzeigen, RE/MAX, CBRE, Colliers, JLL, Savills, and more. # Actor input Schema ## Actor input object example ```json {} ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("rigelbytes/german-realestate-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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("rigelbytes/german-realestate-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call rigelbytes/german-realestate-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=rigelbytes/german-realestate-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "German Real Estate MCP", "description": "MCP server for German real estate β€” 14 Rigel Bytes scrapers (ImmoScout24, Immowelt, Kleinanzeigen, RE/MAX, CBRE, Colliers, JLL, and more). Connect from Cursor, Claude, or VS Code.", "version": "1.1", "x-build-id": "vUe3X2aJU5JCcZccr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/rigelbytes~german-realestate-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-rigelbytes-german-realestate-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/rigelbytes~german-realestate-mcp/runs": { "post": { "operationId": "runs-sync-rigelbytes-german-realestate-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/rigelbytes~german-realestate-mcp/run-sync": { "post": { "operationId": "run-sync-rigelbytes-german-realestate-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```