LinkedIn Scraper MCP Server

An automated, intelligent LinkedIn Job Scraper that runs as a Model Context Protocol (MCP) server. This allows AI assistants (like Claude) to actively search, filter, and extract job listings from LinkedIn using an automated headless browser.

🚀 Hosted Server URL

If you want to connect your AI directly to the hosted remote server, use this SSE endpoint:

👉 [INSERT YOUR HUGGING FACE SPACE URL HERE]

Related MCP server: LinkedIn MCP Server

✨ Features

• Playwright Automation: Safely navigates LinkedIn and loads dynamic content using a headless Chromium browser.

• Semantic AI Filtering: Integrates with Groq (llama-3.3-70b-versatile) to semantically filter irrelevant job titles, enforcing strict requirements (e.g. keeping "Fresher" while rejecting "Senior" level).

• Deduplication: Automatically cleans and removes duplicate job postings.

• Rich Job Details: Extracts deep metadata including Salary, Employment Type, Seniority, Applicant count, and Required Skills.

• Dual Connection Modes: Supports both SSE (for remote web clients) and Stdio (for the local Claude Desktop app).

• Dual-Purpose Architecture: Hosts both the MCP Server (at /mcp) and a Next.js Web Dashboard (at /) simultaneously on port 7860.

🖥️ Web Dashboard (UI)

Because this project uses a custom Next.js server architecture, if you visit your server's root URL (or http://localhost:7860), you will see the Next.js web interface! You can use this to build a visual frontend dashboard to track jobs alongside your Claude integration.

🤔 Why use this instead of LinkedIn directly?

If you've ever searched for an entry-level or specific role on LinkedIn, you know the pain:

1. Irrelevant "Promoted" Jobs: LinkedIn aggressively pushes promoted jobs that often have nothing to do with your search.

2. Broken Seniority Filters: You search for "Intern" or "Junior", but half the results are for "Senior Manager" because the company mentioned "Junior" somewhere deep in the description. Our Groq LLM Semantic Filter fixes this by physically rejecting listings that don't match your true intent.

3. Information Overload: To find the salary, required skills, or applicant count, you normally have to open 50 different tabs. This scraper pulls all of that deep metadata out for you at once.

4. AI-Ready Format: Because the data is returned as structured JSON directly to Claude, Claude can instantly read all 25+ jobs, summarize the market, pick the best matches for your resume, or even start drafting cover letters for them immediately!

💻 Connecting to Claude Desktop (Local)

To run this tool locally on your own machine alongside the Claude Desktop App:

1. Clone this repository.

2. Install dependencies and the browser:

   npm install
   npx playwright install --with-deps chromium

3. Create a .env.local file in the root directory and add your Groq API key:

   GROQ_API_KEY=gsk_your_groq_api_key_here

4. Add the following to your Claude Desktop config file (%APPDATA%\Claude\claude_desktop_config.json):

   {
     "mcpServers": {
       "linkedin-scraper": {
         "command": "npx",
         "args": [
           "tsx",
           "d:/linkedin_Job_Tracker/mcp-claude.ts"
         ]
       }
     }
   }

5. Restart the Claude Desktop app.

🌐 Connecting to a Web MCP Client (Remote)

If you are hosting this on Hugging Face Spaces (or just running the SSE server locally):

1. Start the remote server:

   npm run mcp

2. The server will run on port 7860.

3. In your Web AI interface (like Claude for Enterprise), add a new Custom Connector and point it to the remote URL (or http://localhost:7860/mcp if testing locally).

☁️ Deployment

A Dockerfile is included specifically for deploying this project to Hugging Face Spaces (Docker tier). Simply upload the repository files to a new Docker Space, add your GROQ_API_KEY to the Space Settings -> Variables and secrets, and Hugging Face will automatically build the environment and host the MCP server for you.