# 11880.com Branchenbuch Scraper (`m3web/11880-com-branchenbuch-scraper`) Actor

Actor für 11880.com: findet Unternehmen nach Branche und extrahiert Kontaktdaten (E‑Mail, Telefon, Adresse). EN: Scraper for German companies listed in the 11880.com Branchenbuch (business directory).

- **URL**: https://apify.com/m3web/11880-com-branchenbuch-scraper.md
- **Developed by:** [M3Web](https://apify.com/m3web) (community)
- **Categories:** Lead generation, E-commerce, Automation
- **Features:** Limited permissions
- **Stats:** 12 total users, 2 monthly users, 100.0% runs succeeded, 4 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $3.30 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🏢 11880.com Branchenbuch Scraper

***

### ✨ Einführung

Dieser Actor durchsucht 11880.com nach Unternehmen einer gewählten Branche und extrahiert strukturierte Kontaktdaten.\
Er hilft Ihnen, schnell Firmeninformationen wie **E‑Mail, Telefon und Adresse** zu sammeln.

***

### ⚙️ Funktionsweise

Sie müssen keine URLs bereitstellen. Folgen Sie einfach diesen drei Schritten im Actor‑Input:

1️⃣ **Nur Firmen mit E‑Mail abrufen (ON/OFF)**\
Standard ist **ON**. Schalten Sie auf OFF, wenn Sie auch Firmen ohne E‑Mail-Adresse möchten.

2️⃣ **Branche auswählen (Pflichtfeld)**\
Wählen Sie die gewünschte Branche über das Dropdown-Menü.

3️⃣ **Städte auswählen**\
Wählen Sie eine oder mehrere Städte aus den unten aufgeführten Bundesländern.\
Die Städte sind nach Einwohnerzahl sortiert – von den größten bis zu den kleinsten. Die kleinsten Städte in dieser Liste haben **Einwohner / population: ~50k**.\
Der Actor sammelt E‑Mails und weitere Kontaktdaten für die gewählten Orte.

***

### 💡 Tipp für erste Nutzung & Hinweis zu Herborn

Wenn Sie den Actor zum ersten Mal testen, wählen Sie bitte eine **beliebte Branche** und eine **kleinere Stadt**.\
Großstädte wie Berlin können 5–15 Minuten dauern und verursachen höhere Kosten, was für einen einfachen Test oft unnötig ist.

Die Stadt **Herborn** (ca. 20.000 Einwohner) ist standardmäßig aktiviert, damit die automatische Prüfung des Actors auf der Apify‑Plattform erfolgreich läuft und Sie sofort ein kleines Test‑Ergebnis erhalten.\
Sobald Sie eigene Städte auswählen, **deaktivieren Sie bitte Herborn**, da sie nur für Testzwecke vorgesehen ist.

***

### 📊 Beispielausgabe

```json
{
  "company_name": "Musterfirma Berlin GmbH",
  "email": "kontakt@musterfirma-berlin.de",
  "telephone": "(030) 1234567",
  "postalCode": "10115",
  "addressLocality": "Berlin",
  "addressRegion": "Berlin",
  "streetAddress": "Musterstraße 1",
  "rating": {
    "worstRating": 1,
    "bestRating": 5,
    "ratingValue": 4,
    "reviewCount": 12
  },
  "source": "www.11880.com"
}
```

***

### 📌 Hinweis

Dieser Actor wurde unter Beachtung der [11880.com robots.txt](https://www.11880.com/robots.txt) Regeln am **09.04.2026** erstellt.

***

***

## English Version

***

### ✨ Introduction

This Actor scrapes **German companies** from 11880.com in a selected industry and extracts structured contact data.\
It helps you quickly collect business information such as **email, phone, and address**.

***

### ⚙️ How it works

You don’t need to provide URLs manually. Just follow three simple steps in the Actor input:

1️⃣ **Scrape only companies with email (ON/OFF)**\
Default is **ON**. Turn OFF if you also want companies without email addresses.

2️⃣ **Choose industry (required)**\
Use the dropdown menu to select the industry you want to scrape.

3️⃣ **Choose cities**\
Select one or more cities from the states listed below.\
The cities are ordered by population, from largest to smallest. The smallest cities in this list have **population: ~50k**.\
The Actor will scrape emails and other contact data for the chosen locations.

***

### 💡 Suggestion for first‑time users & Note about Herborn

If you are testing this Actor for the first time, choose a **popular industry** and a **smaller city**.\
Large cities like Berlin may take 5–15 minutes and generate higher costs, which is unnecessary for a simple test run.

The city **Herborn** (population ~20,000) is enabled by default to ensure the Actor passes Apify’s automated testing and to give you a quick, small test result.\
Once you select your own cities, **please turn off Herborn**, since it is only meant for testing purposes.

***

### 📊 Example output

```json
{
  "company_name": "Musterfirma Berlin GmbH",
  "email": "kontakt@musterfirma-berlin.de",
  "telephone": "(030) 1234567",
  "postalCode": "10115",
  "addressLocality": "Berlin",
  "addressRegion": "Berlin",
  "streetAddress": "Musterstraße 1",
  "rating": {
    "worstRating": 1,
    "bestRating": 5,
    "ratingValue": 4,
    "reviewCount": 12
  },
  "source": "www.11880.com"
}
```

***

### 📌 Notes

This Actor was created in compliance with [11880.com robots.txt](https://www.11880.com/robots.txt) rules on **09.04.2026**.

# Actor input Schema

## `startUrls` (type: `array`):

This field is not used.

## `email` (type: `boolean`):

Wenn aktiviert, werden nur Firmen mit E-Mail-Adresse gesammelt. | EN: When enabled, only companies with an email address will be scraped.

## `branche` (type: `string`):

Bitte wählen Sie eine Branche aus der Liste. | EN: Please select an industry from the list.

## `herborn` (type: `boolean`):

Standard-Teststadt für die Apify-Automatisierung. Diese kleine Stadt ist standardmäßig aktiviert, um sicherzustellen, dass der Actor die automatisierten Tests besteht. | EN: Default test city for Apify automation. This small city is enabled by default to ensure the Actor passes automated testing.

## `chooseCities` (type: `string`):

Hidden helper field

## `bbbhms` (type: `string`):

Städte in Berlin, Bremen, Hamburg, Saarland

## `berlin` (type: `boolean`):

Einwohner / population: >3.5 M

## `bremen` (type: `boolean`):

Einwohner / population: ~560k

## `hamburg` (type: `boolean`):

Einwohner / population: ~1.9 M

## `saarbruecken` (type: `boolean`):

Einwohner / population: ~180k

## `badenWuerttemberg` (type: `string`):

Städte in Baden-Württemberg

## `stuttgart` (type: `boolean`):

Einwohner / population: ~600k

## `mannheim` (type: `boolean`):

Einwohner / population: ~310k

## `karlsruhe` (type: `boolean`):

Einwohner / population: ~310k

## `freiburg` (type: `boolean`):

Einwohner / population: ~230k

## `heidelberg` (type: `boolean`):

Einwohner / population: ~160k

## `ulm` (type: `boolean`):

Einwohner / population: ~130k

## `heilbronn` (type: `boolean`):

Einwohner / population: ~130k

## `pforzheim` (type: `boolean`):

Einwohner / population: ~130k

## `reutlingen` (type: `boolean`):

Einwohner / population: ~120k

## `esslingen-am-neckar` (type: `boolean`):

Einwohner / population: ~95k

## `ludwigsburg` (type: `boolean`):

Einwohner / population: ~90k

## `tuebingen` (type: `boolean`):

Einwohner / population: ~90k

## `villingen-schwenningen` (type: `boolean`):

Einwohner / population: ~90k

## `konstanz` (type: `boolean`):

Einwohner / population: ~85k

## `aalen` (type: `boolean`):

Einwohner / population: ~70k

## `schwaebisch-gmuend` (type: `boolean`):

Einwohner / population: ~65k

## `offenburg` (type: `boolean`):

Einwohner / population: ~60k

## `friedrichshafen` (type: `boolean`):

Einwohner / population: ~60k

## `sindelfingen` (type: `boolean`):

Einwohner / population: ~60k

## `goeppingen` (type: `boolean`):

Einwohner / population: ~60k

## `waiblingen` (type: `boolean`):

Einwohner / population: ~60k

## `baden-baden` (type: `boolean`):

Einwohner / population: ~55k

## `boeblingen` (type: `boolean`):

Einwohner / population: ~50k

## `heidenheim` (type: `boolean`):

Einwohner / population: ~50k

## `ravensburg` (type: `boolean`):

Einwohner / population: ~50k

## `loerrach` (type: `boolean`):

Einwohner / population: ~50k

## `bayern` (type: `string`):

Städte in Bayern

## `muenchen` (type: `boolean`):

Einwohner / population: ~1.5 M

## `nuernberg` (type: `boolean`):

Einwohner / population: ~510k

## `augsburg` (type: `boolean`):

Einwohner / population: ~300k

## `regensburg` (type: `boolean`):

Einwohner / population: ~150k

## `ingolstadt` (type: `boolean`):

Einwohner / population: ~130k

## `fuerth` (type: `boolean`):

Einwohner / population: ~130k

## `wuerzburg` (type: `boolean`):

Einwohner / population: ~130k

## `erlangen` (type: `boolean`):

Einwohner / population: ~110k

## `bamberg` (type: `boolean`):

Einwohner / population: ~75k

## `bayreuth` (type: `boolean`):

Einwohner / population: ~75k

## `aschaffenburg` (type: `boolean`):

Einwohner / population: ~70k

## `landshut` (type: `boolean`):

Einwohner / population: ~70k

## `kempten` (type: `boolean`):

Einwohner / population: ~65k

## `rosenheim` (type: `boolean`):

Einwohner / population: ~65k

## `neu-ulm` (type: `boolean`):

Einwohner / population: ~60k

## `schweinfurt` (type: `boolean`):

Einwohner / population: ~55k

## `passau` (type: `boolean`):

Einwohner / population: ~50k

## `brandenburg` (type: `string`):

Städte in Brandenburg

## `potsdam` (type: `boolean`):

Einwohner / population: ~180k

## `cottbus` (type: `boolean`):

Einwohner / population: ~95k

## `brandenburg-an-der-havel` (type: `boolean`):

Einwohner / population: ~75k

## `frankfurt-oder` (type: `boolean`):

Einwohner / population: ~55k

## `oranienburg` (type: `boolean`):

Einwohner / population: ~50k

## `hessen` (type: `string`):

Städte in Hessen

## `frankfurt` (type: `boolean`):

Einwohner / population: ~750k

## `wiesbaden` (type: `boolean`):

Einwohner / population: ~280k

## `kassel` (type: `boolean`):

Einwohner / population: ~200k

## `darmstadt` (type: `boolean`):

Einwohner / population: ~160k

## `offenbach` (type: `boolean`):

Einwohner / population: ~130k

## `hanau` (type: `boolean`):

Einwohner / population: ~95k

## `giessen` (type: `boolean`):

Einwohner / population: ~90k

## `marburg` (type: `boolean`):

Einwohner / population: ~75k

## `fulda` (type: `boolean`):

Einwohner / population: ~65k

## `ruesselsheim` (type: `boolean`):

Einwohner / population: ~65k

## `bad-homburg` (type: `boolean`):

Einwohner / population: ~55k

## `wetzlar` (type: `boolean`):

Einwohner / population: ~55k

## `mecklenburg-vorpommern` (type: `string`):

Städte in Mecklenburg-Vorpommern

## `rostock` (type: `boolean`):

Einwohner / population: ~210k

## `schwerin` (type: `boolean`):

Einwohner / population: ~100k

## `neubrandenburg` (type: `boolean`):

Einwohner / population: ~60k

## `greifswald` (type: `boolean`):

Einwohner / population: ~55k

## `stralsund` (type: `boolean`):

Einwohner / population: ~55k

## `niedersachsen` (type: `string`):

Städte in Niedersachsen

## `hannover` (type: `boolean`):

Einwohner / population: ~540k

## `braunschweig` (type: `boolean`):

Einwohner / population: ~250k

## `oldenburg` (type: `boolean`):

Einwohner / population: ~170k

## `osnabrueck` (type: `boolean`):

Einwohner / population: ~160k

## `wolfsburg` (type: `boolean`):

Einwohner / population: ~130k

## `goettingen` (type: `boolean`):

Einwohner / population: ~130k

## `salzgitter` (type: `boolean`):

Einwohner / population: ~100k

## `hildesheim` (type: `boolean`):

Einwohner / population: ~100k

## `delmenhorst` (type: `boolean`):

Einwohner / population: ~80k

## `wilhelmshaven` (type: `boolean`):

Einwohner / population: ~75k

## `lueneburg` (type: `boolean`):

Einwohner / population: ~75k

## `celle` (type: `boolean`):

Einwohner / population: ~70k

## `garbsen` (type: `boolean`):

Einwohner / population: ~60k

## `hameln` (type: `boolean`):

Einwohner / population: ~60k

## `nordhorn` (type: `boolean`):

Einwohner / population: ~60k

## `lingen` (type: `boolean`):

Einwohner / population: ~55k

## `langenhagen` (type: `boolean`):

Einwohner / population: ~55k

## `wolfenbuettel` (type: `boolean`):

Einwohner / population: ~55k

## `peine` (type: `boolean`):

Einwohner / population: ~50k

## `cuxhaven` (type: `boolean`):

Einwohner / population: ~50k

## `emden` (type: `boolean`):

Einwohner / population: ~50k

## `stade` (type: `boolean`):

Einwohner / population: ~50k

## `goslar` (type: `boolean`):

Einwohner / population: ~50k

## `nordrheinWestfalen` (type: `string`):

Städte in Nordrhein-Westfalen

## `koeln` (type: `boolean`):

Einwohner / population: ~1.1 M

## `duesseldorf` (type: `boolean`):

Einwohner / population: ~600k

## `dortmund` (type: `boolean`):

Einwohner / population: ~600k

## `essen` (type: `boolean`):

Einwohner / population: ~600k

## `duisburg` (type: `boolean`):

Einwohner / population: ~500k

## `bochum` (type: `boolean`):

Einwohner / population: ~360k

## `wuppertal` (type: `boolean`):

Einwohner / population: ~350k

## `bielefeld` (type: `boolean`):

Einwohner / population: ~340k

## `bonn` (type: `boolean`):

Einwohner / population: ~330k

## `muenster` (type: `boolean`):

Einwohner / population: ~320k

## `moenchengladbach` (type: `boolean`):

Einwohner / population: ~260k

## `gelsenkirchen` (type: `boolean`):

Einwohner / population: ~260k

## `aachen` (type: `boolean`):

Einwohner / population: ~250k

## `krefeld` (type: `boolean`):

Einwohner / population: ~230k

## `oberhausen` (type: `boolean`):

Einwohner / population: ~210k

## `hagen` (type: `boolean`):

Einwohner / population: ~190k

## `hamm` (type: `boolean`):

Einwohner / population: ~180k

## `muelheim` (type: `boolean`):

Einwohner / population: ~170k

## `leverkusen` (type: `boolean`):

Einwohner / population: ~160k

## `solingen` (type: `boolean`):

Einwohner / population: ~160k

## `herne` (type: `boolean`):

Einwohner / population: ~160k

## `neuss` (type: `boolean`):

Einwohner / population: ~160k

## `paderborn` (type: `boolean`):

Einwohner / population: ~150k

## `bottrop` (type: `boolean`):

Einwohner / population: ~120k

## `remscheid` (type: `boolean`):

Einwohner / population: ~110k

## `bergisch-gladbach` (type: `boolean`):

Einwohner / population: ~110k

## `recklinghausen` (type: `boolean`):

Einwohner / population: ~110k

## `moers` (type: `boolean`):

Einwohner / population: ~100k

## `siegen` (type: `boolean`):

Einwohner / population: ~100k

## `guetersloh` (type: `boolean`):

Einwohner / population: ~100k

## `dueren` (type: `boolean`):

Einwohner / population: ~95k

## `iserlohn` (type: `boolean`):

Einwohner / population: ~90k

## `witten` (type: `boolean`):

Einwohner / population: ~90k

## `ratingen` (type: `boolean`):

Einwohner / population: ~90k

## `marl` (type: `boolean`):

Einwohner / population: ~85k

## `luenen` (type: `boolean`):

Einwohner / population: ~85k

## `minden` (type: `boolean`):

Einwohner / population: ~85k

## `velbert` (type: `boolean`):

Einwohner / population: ~85k

## `viersen` (type: `boolean`):

Einwohner / population: ~80k

## `rheine` (type: `boolean`):

Einwohner / population: ~80k

## `troisdorf` (type: `boolean`):

Einwohner / population: ~75k

## `gladbeck` (type: `boolean`):

Einwohner / population: ~75k

## `dorsten` (type: `boolean`):

Einwohner / population: ~75k

## `arnsberg` (type: `boolean`):

Einwohner / population: ~75k

## `detmold` (type: `boolean`):

Einwohner / population: ~75k

## `castrop-rauxel` (type: `boolean`):

Einwohner / population: ~75k

## `bocholt` (type: `boolean`):

Einwohner / population: ~75k

## `luedenscheid` (type: `boolean`):

Einwohner / population: ~70k

## `lippstadt` (type: `boolean`):

Einwohner / population: ~70k

## `herford` (type: `boolean`):

Einwohner / population: ~70k

## `dinslaken` (type: `boolean`):

Einwohner / population: ~65k

## `kerpen` (type: `boolean`):

Einwohner / population: ~65k

## `grevenbroich` (type: `boolean`):

Einwohner / population: ~65k

## `dormagen` (type: `boolean`):

Einwohner / population: ~65k

## `huerth` (type: `boolean`):

Einwohner / population: ~60k

## `bergheim` (type: `boolean`):

Einwohner / population: ~60k

## `herten` (type: `boolean`):

Einwohner / population: ~60k

## `wesel` (type: `boolean`):

Einwohner / population: ~60k

## `euskirchen` (type: `boolean`):

Einwohner / population: ~60k

## `langenfeld` (type: `boolean`):

Einwohner / population: ~60k

## `unna` (type: `boolean`):

Einwohner / population: ~60k

## `stolberg` (type: `boolean`):

Einwohner / population: ~60k

## `eschweiler` (type: `boolean`):

Einwohner / population: ~60k

## `meerbusch` (type: `boolean`):

Einwohner / population: ~55k

## `sankt-augustin` (type: `boolean`):

Einwohner / population: ~55k

## `pulheim` (type: `boolean`):

Einwohner / population: ~55k

## `hilden` (type: `boolean`):

Einwohner / population: ~55k

## `bad-salzuflen` (type: `boolean`):

Einwohner / population: ~55k

## `hattingen` (type: `boolean`):

Einwohner / population: ~55k

## `ahlen` (type: `boolean`):

Einwohner / population: ~55k

## `frechen` (type: `boolean`):

Einwohner / population: ~50k

## `menden` (type: `boolean`):

Einwohner / population: ~50k

## `ibbenbueren` (type: `boolean`):

Einwohner / population: ~50k

## `gummersbach` (type: `boolean`):

Einwohner / population: ~50k

## `bad-oeynhausen` (type: `boolean`):

Einwohner / population: ~50k

## `gronau` (type: `boolean`):

Einwohner / population: ~50k

## `willich` (type: `boolean`):

Einwohner / population: ~50k

## `rheinlandPfalz` (type: `string`):

Städte in Rheinland-Pfalz

## `mainz` (type: `boolean`):

Einwohner / population: ~210k

## `ludwigshafen` (type: `boolean`):

Einwohner / population: ~170k

## `koblenz` (type: `boolean`):

Einwohner / population: ~120k

## `trier` (type: `boolean`):

Einwohner / population: ~110k

## `kaiserslautern` (type: `boolean`):

Einwohner / population: ~100k

## `worms` (type: `boolean`):

Einwohner / population: ~85k

## `neuwied` (type: `boolean`):

Einwohner / population: ~65k

## `bad-kreuznach` (type: `boolean`):

Einwohner / population: ~55k

## `neustadt` (type: `boolean`):

Einwohner / population: ~55k

## `sachsen` (type: `string`):

Städte in Sachsen

## `leipzig` (type: `boolean`):

Einwohner / population: ~600k

## `dresden` (type: `boolean`):

Einwohner / population: ~560k

## `chemnitz` (type: `boolean`):

Einwohner / population: ~250k

## `zwickau` (type: `boolean`):

Einwohner / population: ~90k

## `plauen` (type: `boolean`):

Einwohner / population: ~65k

## `goerlitz` (type: `boolean`):

Einwohner / population: ~55k

## `sachsenAnhalt` (type: `string`):

Städte in Sachsen-Anhalt

## `halle` (type: `boolean`):

Einwohner / population: ~240k

## `magdeburg` (type: `boolean`):

Einwohner / population: ~240k

## `dessau-rosslau` (type: `boolean`):

Einwohner / population: ~75k

## `schleswigHolstein` (type: `string`):

Städte in Schleswig-Holstein

## `kiel` (type: `boolean`):

Einwohner / population: ~250k

## `luebeck` (type: `boolean`):

Einwohner / population: ~220k

## `flensburg` (type: `boolean`):

Einwohner / population: ~95k

## `norderstedt` (type: `boolean`):

Einwohner / population: ~85k

## `neumuenster` (type: `boolean`):

Einwohner / population: ~80k

## `elmshorn` (type: `boolean`):

Einwohner / population: ~50k

## `thueringen` (type: `string`):

Städte in Thüringen

## `erfurt` (type: `boolean`):

Einwohner / population: ~210k

## `jena` (type: `boolean`):

Einwohner / population: ~110k

## `gera` (type: `boolean`):

Einwohner / population: ~95k

## `weimar` (type: `boolean`):

Einwohner / population: ~65k

## Actor input object example

```json
{
  "startUrls": [],
  "email": true,
  "branche": "arzt",
  "herborn": true,
  "berlin": false,
  "bremen": false,
  "hamburg": false,
  "saarbruecken": false,
  "stuttgart": false,
  "mannheim": false,
  "karlsruhe": false,
  "freiburg": false,
  "heidelberg": false,
  "ulm": false,
  "heilbronn": false,
  "pforzheim": false,
  "reutlingen": false,
  "esslingen-am-neckar": false,
  "ludwigsburg": false,
  "tuebingen": false,
  "villingen-schwenningen": false,
  "konstanz": false,
  "aalen": false,
  "schwaebisch-gmuend": false,
  "offenburg": false,
  "friedrichshafen": false,
  "sindelfingen": false,
  "goeppingen": false,
  "waiblingen": false,
  "baden-baden": false,
  "boeblingen": false,
  "heidenheim": false,
  "ravensburg": false,
  "loerrach": false,
  "muenchen": false,
  "nuernberg": false,
  "augsburg": false,
  "regensburg": false,
  "ingolstadt": false,
  "fuerth": false,
  "wuerzburg": false,
  "erlangen": false,
  "bamberg": false,
  "bayreuth": false,
  "aschaffenburg": false,
  "landshut": false,
  "kempten": false,
  "rosenheim": false,
  "neu-ulm": false,
  "schweinfurt": false,
  "passau": false,
  "potsdam": false,
  "cottbus": false,
  "brandenburg-an-der-havel": false,
  "frankfurt-oder": false,
  "oranienburg": false,
  "frankfurt": false,
  "wiesbaden": false,
  "kassel": false,
  "darmstadt": false,
  "offenbach": false,
  "hanau": false,
  "giessen": false,
  "marburg": false,
  "fulda": false,
  "ruesselsheim": false,
  "bad-homburg": false,
  "wetzlar": false,
  "rostock": false,
  "schwerin": false,
  "neubrandenburg": false,
  "greifswald": false,
  "stralsund": false,
  "hannover": false,
  "braunschweig": false,
  "oldenburg": false,
  "osnabrueck": false,
  "wolfsburg": false,
  "goettingen": false,
  "salzgitter": false,
  "hildesheim": false,
  "delmenhorst": false,
  "wilhelmshaven": false,
  "lueneburg": false,
  "celle": false,
  "garbsen": false,
  "hameln": false,
  "nordhorn": false,
  "lingen": false,
  "langenhagen": false,
  "wolfenbuettel": false,
  "peine": false,
  "cuxhaven": false,
  "emden": false,
  "stade": false,
  "goslar": false,
  "koeln": false,
  "duesseldorf": false,
  "dortmund": false,
  "essen": false,
  "duisburg": false,
  "bochum": false,
  "wuppertal": false,
  "bielefeld": false,
  "bonn": false,
  "muenster": false,
  "moenchengladbach": false,
  "gelsenkirchen": false,
  "aachen": false,
  "krefeld": false,
  "oberhausen": false,
  "hagen": false,
  "hamm": false,
  "muelheim": false,
  "leverkusen": false,
  "solingen": false,
  "herne": false,
  "neuss": false,
  "paderborn": false,
  "bottrop": false,
  "remscheid": false,
  "bergisch-gladbach": false,
  "recklinghausen": false,
  "moers": false,
  "siegen": false,
  "guetersloh": false,
  "dueren": false,
  "iserlohn": false,
  "witten": false,
  "ratingen": false,
  "marl": false,
  "luenen": false,
  "minden": false,
  "velbert": false,
  "viersen": false,
  "rheine": false,
  "troisdorf": false,
  "gladbeck": false,
  "dorsten": false,
  "arnsberg": false,
  "detmold": false,
  "castrop-rauxel": false,
  "bocholt": false,
  "luedenscheid": false,
  "lippstadt": false,
  "herford": false,
  "dinslaken": false,
  "kerpen": false,
  "grevenbroich": false,
  "dormagen": false,
  "huerth": false,
  "bergheim": false,
  "herten": false,
  "wesel": false,
  "euskirchen": false,
  "langenfeld": false,
  "unna": false,
  "stolberg": false,
  "eschweiler": false,
  "meerbusch": false,
  "sankt-augustin": false,
  "pulheim": false,
  "hilden": false,
  "bad-salzuflen": false,
  "hattingen": false,
  "ahlen": false,
  "frechen": false,
  "menden": false,
  "ibbenbueren": false,
  "gummersbach": false,
  "bad-oeynhausen": false,
  "gronau": false,
  "willich": false,
  "mainz": false,
  "ludwigshafen": false,
  "koblenz": false,
  "trier": false,
  "kaiserslautern": false,
  "worms": false,
  "neuwied": false,
  "bad-kreuznach": false,
  "neustadt": false,
  "leipzig": false,
  "dresden": false,
  "chemnitz": false,
  "zwickau": false,
  "plauen": false,
  "goerlitz": false,
  "halle": false,
  "magdeburg": false,
  "dessau-rosslau": false,
  "kiel": false,
  "luebeck": false,
  "flensburg": false,
  "norderstedt": false,
  "neumuenster": false,
  "elmshorn": false,
  "erfurt": false,
  "jena": false,
  "gera": false,
  "weimar": false
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("m3web/11880-com-branchenbuch-scraper").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "startUrls": [] }

# Run the Actor and wait for it to finish
run = client.actor("m3web/11880-com-branchenbuch-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": []
}' |
apify call m3web/11880-com-branchenbuch-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,m3web/11880-com-branchenbuch-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/RtIbFSMV5FnW5NaWz/builds/20Ka0QfVkON0N0lT5/openapi.json