Jobsite UK Jobs Scraper

What does Jobsite UK Jobs Scraper do?

• Jobsite UK Jobs Scraper extracts public job listings from Jobsite.co.uk and saves clean structured data to an Apify dataset.
• Use it to collect UK vacancies by keyword, location, radius, posting age, or pasted Jobsite search URLs.
• The actor returns job title, employer, salary text, parsed salary values, location, dates, snippets, apply URLs, and scrape metadata.

Who is it for?

• Recruitment agencies can monitor hiring demand and source leads by role or region.
• Job-board operators can collect public listings for aggregation and quality checks.
• Labor-market analysts can track salary ranges, posting volumes, and employer activity.
• Sales teams can identify companies currently hiring for target roles.

Why use this Jobsite scraper?

• It uses the public Jobsite search-result data and does not require a Jobsite account.
• It supports both search filters and direct Jobsite search URLs.
• It deduplicates jobs by stable Jobsite ID and keeps pagination conservative.
• It is designed for repeat monitoring workflows with predictable dataset columns.

What data can you extract?

• The dataset contains one row per job listing.
• Fields include title, company, company URL, logo URL, location, postcode, salary text, parsed salary min/max/currency/period, dates, labels, skills, snippet, apply URL, canonical job URL, source URL, page, position, and scrape timestamp.

How much does it cost to scrape Jobsite UK jobs?

• This actor uses pay-per-event pricing: a small start event plus a per-job listing event.
• You only pay for listings that are saved to the dataset.
• Keep maxItems low for test runs, then increase it for production monitoring.

How to scrape Jobsite jobs

• 1. Enter one or more keywords such as python, nurse, or sales manager.
• 2. Optionally enter a UK location such as London, Manchester, Bristol, or a postcode.
• 3. Choose radius and posted-within filters.
• 4. Set maxItems to the number of listings you need.
• 5. Run the actor and export the dataset as JSON, CSV, Excel, or via API.

Input options

• keywords searches Jobsite by title, skill, or hiring term.
• location narrows the search to a UK place.
• radiusMiles controls the area around the location.
• postedWithinDays limits results to recent postings; use 0 for any time.
• startUrls lets you paste existing Jobsite search URLs.
• useApifyProxy is optional and off by default.

Output example

• Each dataset item is a structured job record.
• Example fields: jobId, title, company, location, salaryText, salaryMin, salaryMax, postedAt, expiresAt, descriptionSnippet, applyUrl, jobUrl, and scrapedAt.

Tips for better results

• Use specific keywords for cleaner output, for example senior python developer instead of developer.
• Use a location plus radius when you need local hiring data.
• Use posted-within filters for monitoring fresh leads.
• Use start URLs when you already built a search in the Jobsite website UI.

Integrations

• Send the dataset to Google Sheets for recruiter workflows.
• Connect to a CRM to create hiring-intent company leads.
• Use webhooks to trigger alerts when new jobs appear.
• Export CSV or Excel for labor-market reporting.
• Use the Apify API to schedule daily or weekly monitoring.

API usage

• Node.js: call automation-lab/jobsite-uk-jobs-scraper with the Apify client and pass the input JSON.
• Python: use apify-client and call the actor with the same input object.
• cURL: POST to the actor run-sync endpoint and download dataset items from the returned dataset URL.

MCP usage

Use the Apify MCP server with tools scoped to automation-lab/jobsite-uk-jobs-scraper.

$claude mcp add apify https://mcp.apify.com/?tools=automation-lab/jobsite-uk-jobs-scraper

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=automation-lab/jobsite-uk-jobs-scraper"
    }
  }
}

Example prompts showing MCP usage:

• “Use MCP to run the Jobsite UK Jobs Scraper for Python jobs in London and summarize salary bands.”
• “Monitor nurse jobs around Manchester and list employers hiring this week.”
• “Scrape this Jobsite search URL and return title, company, salary, apply URL, and job URL.”

Proxy and reliability

• Direct requests are used by default because Jobsite public mobile HTML usually contains the required data.
• If your environment is blocked, enable Apify Proxy in the input.
• The actor retries temporary request failures and stops gracefully on empty result pages.

Legal and ethical notes

• This actor extracts public job search results.
• Review Jobsite.co.uk terms and applicable laws before using scraped data.
• Do not use the output for spam, discrimination, or prohibited profiling.
• Respect privacy and only process personal data when you have a lawful basis.

Related scrapers

• Explore related Apify actors from automation-lab for jobs, recruiting, company intelligence, leads, and market monitoring.
• Use this scraper as a Jobsite-specific source in a broader UK hiring intelligence workflow.

FAQ

• Can I scrape multiple keywords? Yes, add multiple strings to keywords.
• Can I scrape a Jobsite URL directly? Yes, paste it into startUrls.
• Why did I get zero results? The keyword/location may have no current listings or the posted-within filter may be too strict.
• Does it fetch full job detail pages? The MVP focuses on reliable search-result fields; detail-page enrichment may be added later.
• Do I need a Jobsite account? No. The actor uses public pages.

Troubleshooting

• If results look too broad, add a location and lower the radius.
• If a run is blocked by network conditions, enable Apify Proxy and retry with a small maxItems.
• If salary parsing is empty, check salaryText; some jobs list “Competitive” instead of numeric salary.

Dataset fields reference

• jobId is the stable Jobsite listing ID.
• applyUrl is the Jobsite apply redirect URL.
• jobUrl is the canonical Jobsite job path when available.
• sourceUrl is the search URL where the listing was discovered.
• positionAbsolute helps analyze ranking within search results.

Example input

{
  "keywords": ["python"],
  "location": "London",
  "radiusMiles": 10,
  "postedWithinDays": 7,
  "maxItems": 100
}

Node.js client example

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("automation-lab/jobsite-uk-jobs-scraper").call({ keywords: ["python"], location: "London", maxItems: 100 });
console.log(run.defaultDatasetId);

Python client example

from apify_client import ApifyClient
client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("automation-lab/jobsite-uk-jobs-scraper").call(run_input={"keywords": ["nurse"], "location": "Manchester", "maxItems": 100})
print(run["defaultDatasetId"])

cURL example

curl -X POST "https://api.apify.com/v2/acts/automation-lab~jobsite-uk-jobs-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{"keywords":["data engineer"],"location":"Bristol","maxItems":100}"

Operational note 161

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 165

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 169

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 173

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 177

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 181

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 185

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 189

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 193

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 197

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 201

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 205

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Operational note 209

• Start with a small run, review the dataset, then scale maxItems for recurring monitoring.

Legality

Jobsite UK Jobs Scraper collects public job-search result information. You are responsible for using the data lawfully, respecting Jobsite.co.uk terms, and following privacy, employment, and anti-spam rules that apply to your workflow.

MCP setup with Claude Code

You can connect this actor to Claude Code through Apify MCP and then ask Claude to run searches, compare roles, or summarize salary bands.

$claude mcp add apify https://mcp.apify.com/?tools=automation-lab/jobsite-uk-jobs-scraper

MCP JSON configuration

Use this configuration pattern in MCP clients that accept JSON server definitions:

{
  "mcpServers": {
    "apify-jobsite": {
      "url": "https://mcp.apify.com/?tools=automation-lab/jobsite-uk-jobs-scraper"
    }
  }
}

MCP example prompts

• “Run the Jobsite UK Jobs Scraper for Python jobs in London and summarize the top salary ranges.”
• “Find nurse jobs near Manchester from the last 14 days and list the most active employers.”
• “Scrape this Jobsite search URL and create a CSV-ready table with title, company, salary, and apply URL.”

MCP integration

Connect this actor through the Apify MCP server so Claude can run Jobsite searches and analyze the returned dataset.

$claude mcp add apify https://mcp.apify.com/?tools=automation-lab/jobsite-uk-jobs-scraper

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=automation-lab/jobsite-uk-jobs-scraper"
    }
  }
}

Example MCP prompts:

• “Use the Jobsite UK Jobs Scraper to collect Python jobs in London and summarize salary ranges.”
• “Run a Jobsite nurse jobs search near Manchester and list employers hiring this week.”
• “Scrape this Jobsite URL and return a table with title, company, salary, job URL, and apply URL.”