LinkedIn Jobs Scraper with Salary & Company Enrichment: MCP config, n8n workflow and API

{
  "searchQueries": [
    "Python Developer"
  ],
  "locations": [
    "United States"
  ],
  "easyApply": false,
  "under10Applicants": false,
  "excludeStaffingAgencies": false,
  "requireSalary": false,
  "requireRecruiter": false,
  "scrapeJobDetails": false,
  "includeCompanyDetails": false,
  "splitLargeSearches": true,
  "maxItems": 3
}

---
name: linkedin-jobs-scraper-with-salary-and-company
description: "LinkedIn job postings with parsed yearly salary ranges and their source, applicant counts, Easy Apply status, seniority, posting expiry dates when LinkedIn exposes them, recruiter, and the employer's LinkedIn profile (size, industry, founding year, offices, related pages). Runs the Apify Actor nimbryxo-studio/linkedin-jobs-scraper-with-salary-and-company. Use when the user needs LinkedIn Jobs data with fields such as title, companyName, location, salary, salarySource, applicantCount and an Apify token is available."
compatibility: "Needs network access, curl, and an APIFY_TOKEN environment variable. Runs are billed to the token owner's Apify account."
metadata:
  actor: "nimbryxo-studio/linkedin-jobs-scraper-with-salary-and-company"
  store-url: "https://apify.com/nimbryxo-studio/linkedin-jobs-scraper-with-salary-and-company"
---

# LinkedIn Jobs Scraper with Salary & Company Enrichment

Use this skill to fetch LinkedIn Jobs data with the Apify Actor `nimbryxo-studio/linkedin-jobs-scraper-with-salary-and-company` and work with the returned rows.

## Before running

1. Check that `APIFY_TOKEN` is set in the environment. Never print it or write it to a file.
2. Build `input.json` from the user's request using the fields below. Keep limits small for a first run.
3. Confirm the search terms or URLs with the user if the request is ambiguous.

## Input fields

| Field | Type | Meaning |
| --- | --- | --- |
| `searchQueries` | array | Job titles, skills or phrases to search for, one per line (e.g. 'data analyst', 'registered nurse'). Each keyword is searched in every location you list, and results are merged without duplicates. |
| `locations` | array | Cities, regions or countries in English (e.g. 'Germany', 'Austin, Texas', 'London Area, United Kingdom'). Every keyword is searched in each location. |
| `easyApply` | boolean | Only jobs you can apply to directly on LinkedIn. |
| `under10Applicants` | boolean | Only jobs that have had fewer than 10 applicants so far. |
| `excludeStaffingAgencies` | boolean | Skip jobs posted by employers in LinkedIn's 'Staffing and Recruiting' industry. |
| `requireSalary` | boolean | Only jobs that state their pay, either in LinkedIn's pay range box or in the job description. |
| `requireRecruiter` | boolean | Only jobs where LinkedIn shows the person who posted the job. |
| `scrapeJobDetails` | boolean | Open each job to add the full description, salary, seniority, job type, industry, applicant count, recruiter and Easy Apply status. Turn off for a fast list of titles, companies, locations and dates only. |
| `includeCompanyDetails` | boolean | Add each employer's LinkedIn profile: industry, size, headquarters, website, founding year, office locations, followers, tagline and description. Fetched once per company. |
| `splitLargeSearches` | boolean | LinkedIn returns at most 1,000 jobs for one search. When a search reaches that limit, continue with the same search limited to the past month, week, 3 days and 24 hours, which surfaces many more jobs (duplicates removed). |
| `maxItems` | integer | Stop after saving this many jobs. A single keyword + location search returns at most 1,000 jobs; add keywords or locations to collect more. |

Example `input.json` (the Actor's published prefill):

```json
{
  "searchQueries": [
    "Python Developer"
  ],
  "locations": [
    "United States"
  ],
  "easyApply": false,
  "under10Applicants": false,
  "excludeStaffingAgencies": false,
  "requireSalary": false,
  "requireRecruiter": false,
  "scrapeJobDetails": false,
  "includeCompanyDetails": false,
  "splitLargeSearches": true,
  "maxItems": 3
}
```

## Run

```bash
curl -sS -X POST "https://api.apify.com/v2/acts/nimbryxo-studio~linkedin-jobs-scraper-with-salary-and-company/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d @input.json > results.json
```

The call waits for the run to finish and returns the dataset items as a JSON array. If the run needs more than 300 seconds the API answers with HTTP 408; then start the run with `POST /v2/acts/nimbryxo-studio~linkedin-jobs-scraper-with-salary-and-company/runs`, poll `GET /v2/actor-runs/{runId}` until `SUCCEEDED`, and read `GET /v2/datasets/{defaultDatasetId}/items`.

## Output

Each item is one record. Published fields include: `title`, `companyName`, `location`, `salary`, `salarySource`, `applicantCount`, `easyApply`, `employmentType`, `seniorityLevel`, `postedDate`, `expiresAt`, `jobUrl`, `salaryRange.currency`, `salaryRange.minYearly`, `salaryRange.maxYearly`, `companyIndustry`, `companySize`, `companyHeadquarters`, `companyFounded`, `companyFollowersCount`.

Summarise what was found, cite the record URLs when present, and say how many items came back. Do not invent values for empty fields.