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Real-time extract of job, company, salary details via Bright Data MCP & OpenAI

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Real-time extract of job, company, salary details via Bright Data MCP & OpenAI preview
Open on n8n.io

Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Notice Community nodes can only be installed on self hosted instances of n8n. Who this is ...

Best for

  • Market Research automation workflows
  • AI Summarization automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-mcp.mcpclient, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.informationextractor, n8n-nodes-base.merge, n8n-nodes-base.function, n8n-nodes-base.httprequest, n8n-nodes-base.readwritefile

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Ranjan Dailata.

Original n8n.io source

1.1 Workflow description

Title
Real-time extract of job, company, salary details via Bright Data MCP & OpenAI
Workflow name
Real-time extract of job, company, salary details via Bright Data MCP & OpenAI

Notice

Community nodes can only be installed on self-hosted instances of n8n.

Who this is for

This workflow automates the real-time extraction of Job Descriptions and Salary Information from job listing pages using Bright Data MCP and analyzes content using OpenAI GPT-4o mini.

This workflow is ideal for:

  • Recruiters & HR Tech Startups: Automate job data collection from public listings

  • Market Intelligence Teams: Analyze compensation trends across companies or geographies

  • Job Boards & Aggregators: Power search results with structured, enriched listings

  • AI Workflow Builders: Extend to other career platforms or automate resume-job match analysis

  • Analysts & Researchers: Track hiring signals and salary benchmarks in real time

What problem is this workflow solving?

Traditional scraping of job portals can be challenging due to cluttered content, anti-scraping measures, and inconsistent formatting. Manually analyzing salary ranges and job descriptions is tedious and error-prone.

This workflow solves the problem by:

  • Simulating user behavior using Bright Data MCP Client to bypass anti-scraping systems

  • Extracting structured, clean job data in Markdown format

  • Using OpenAI GPT-4o mini to analyze and extract precise salary details and refined job descriptions

  • Merging and formatting the result for easy consumption

  • Delivering final output via webhook, Google Sheets, or file system

What this workflow does

Components & Flow

Input Nodes

  • job_search_url: The job listing or search result URL

  • job_role: The title or role being searched for (used in logging/formatting)

MCP Client Operations

  • MCP Salary Data Extractor

    • Simulates browser behavior and scrapes salary-related content (if available)

    • MCP Job Description Extractor

Extracts full job description as structured Markdown content

OpenAI GPT-4o mini Nodes

Salary Information Extractor

  • Uses GPT-4o mini to detect, clean, and standardize salary range data (if any)

Job Description Refiner

  • Extracts role responsibilities, qualifications, and benefits from unstructured text

Company Information Extractor

  • Uses Bright Data MCP and GPT-4o mini to extract the company information

Merge Node

  • Combines the refined job description and extracted salary information into a unified JSON response object

Aggregate node

  • Aggregates the job description and salary information into a single JSON response object

Final Output Handling

The output is handled in three different formats depending on your downstream needs:

  • Save to Disk

    • Output stored with filename including timestamp and job role
  • Google Sheet Update

    • Adds a new row with job role, salary, summary, and link
  • Webhook Notification

    • Pushes merged response to an external system

Pre-conditions

  1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol
  2. You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below.
  3. You need to have the Google Gemini API Key. Visit Google AI Studio
  4. You need to install the Bright Data MCP Server @brightdata/mcp
  5. You need to install the n8n-nodes-mcp

Setup

  1. Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp
  2. Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine.
  3. Sign up at Bright Data.
  4. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.
  5. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel.
  6. In n8n, configure the OpenAi account credentials.
  7. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below.

Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>

How to customize this workflow to your needs

Modify Input Source

  • Change the job_search_url to point to any job board or aggregator

  • Customize job_role to reflect the type of jobs being analyzed

Tweak LLM Prompts (Optional)

  • Refine GPT-4o mini prompts to extract additional fields like benefits, tech stacks, remote eligibility

Change Output Format

  • Customize the merged object to output JSON, CSV, or Markdown based on downstream needs

  • Add additional destinations (e.g., Slack, Airtable, Notion) via n8n nodes

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Type / Role
n8n-nodes-base.manualTrigger - manualTrigger
Config choices
Version 1

Block 2 - Bright Data MCP Client List Tools

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 3 - Set input fields

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 4 - MCP Client for Job Data Extract with Markdown

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 5 - MCP Client for Salary Data Extraction

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 6 - Salary Information Extractor

Type / Role
@n8n/n8n-nodes-langchain.informationExtractor - informationExtractor
Config choices
Version 1.1

Block 7 - Job Description Extractor

Type / Role
@n8n/n8n-nodes-langchain.informationExtractor - informationExtractor
Config choices
Version 1.1

Block 8 - Merge the response

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.1

Block 9 - Create a binary data

Type / Role
n8n-nodes-base.function - function
Config choices
Version 1

Block 10 - Webhook Notification for Job Info

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 11 - Write the salary info to disk

Type / Role
n8n-nodes-base.readWriteFile - readWriteFile
Config choices
Version 1

Block 12 - Update Google Sheets

Type / Role
n8n-nodes-base.googleSheets - googleSheets
Config choices
Version 4.5

Block 13 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 14 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 15 - Sticky Note5

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 16 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 17 - OpenAI Chat Model for Salary Info Extract

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 18 - OpenAI Chat Model for Job Desc Extract

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 19 - Aggregate

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 20 - MCP Client for Company Data Extract with Markdown

Type / Role
n8n-nodes-mcp.mcpClient - mcpClient
Config choices
Version 1

Block 21 - Company Data Extractor

Type / Role
@n8n/n8n-nodes-langchain.informationExtractor - informationExtractor
Config choices
Version 1.1

Block 22 - OpenAI Chat Model for Company Data Extract

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

3. Summary Table

Workflow Real-time extract of job, company, salary details via Bright Data MCP & OpenAI
Complexity advanced
Nodes 22
Categories Market Research, AI Summarization
Author Ranjan Dailata
Published 09 Jun 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/4829/4829.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Real-time extract of job, company, salary details via Bright Data MCP & OpenAI do?

Notice Community nodes can only be installed on self hosted instances of n8n. Who this is ...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI Summarization use case.