LinkedIn MCP Server

LinkedIn MCP Server

A Model Context Protocol (MCP) server that automates LinkedIn job search and profile editing via Playwright browser automation. Built to help DS/ML job seekers keep their LinkedIn profile sharp and run targeted job searches without touching the UI.

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Features

Job Search

• Search jobs by keywords, location, date, and type
• One-click DS/ML internship and new-grad job search presets
• Fetch full job descriptions from listing URLs

Profile Editing

• Update experience entries (title, company, dates, description)
• Update education entries (GPA, description)
• Update project entries (name, description)
• Update headline and about section

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Setup

1. Install dependencies

pip install -r requirements.txt
playwright install chromium

2. Log in to LinkedIn

Run the server and call manual_login via Claude Code. This opens a real browser window — log in manually, then save the session:

# Claude Code will call:
manual_login()   # opens browser → you log in manually
check_session()  # verifies session is active

The session is saved to session.json and reused automatically on future runs.

3. Register as a Claude Code MCP server

Add this to your Claude Code MCP config (~/.claude/mcp_servers.json or via claude mcp add):

{
  "linkedin": {
    "command": "python",
    "args": ["server.py"],
    "cwd": "/path/to/linkedin_mcp"
  }
}

Or run ad-hoc:

python server.py

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Project Structure

linkedin_mcp/
├── server.py          # FastMCP server — all tools + resume/profile data
├── browser.py         # Playwright automation class (LinkedInBrowser)
├── requirements.txt   # Python dependencies
├── .gitignore
└── README.md

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Tools Reference

| Tool | Description | |------|-------------| | check_session | Check if LinkedIn session is still active | | manual_login | Open browser for manual login and save session | | get_profile | Scrape current profile (name, headline, about, experience, education, skills) | | suggest_profile_improvements | Compare profile against resume data and return suggestions | | update_experience | Update all experience entries with titles, dates, and descriptions | | update_education | Update education entries with GPA and descriptions | | update_projects | Update all project entries with names and descriptions | | apply_profile_improvements | Update headline and about section | | search_jobs | Search jobs by keyword, location, date filter, and job type | | search_recent_internships | Search DS/ML/AI internships posted in the last week | | search_new_grad_jobs | Search entry-level DS/ML full-time roles | | get_job_details | Fetch full job description from a listing URL |

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Key Technical Notes

• React inputs — uses Playwright .fill(), not JS .value assignment (JS bypasses React state)
• Obfuscated field IDs — field IDs are dynamically resolved by finding label elements by text, then reading their htmlFor attribute
• LinkedIn edit URLs — discovered dynamically from <a> tags on details pages; edit form renders immediately when navigated to directly
• SPA routing — LinkedIn uses history.pushState; polls page.url instead of waiting for Playwright navigation events
• Experience description — contenteditable div filled via execCommand('insertText'); education and project descriptions are regular <textarea> elements

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Dependencies

| Package | Purpose | |---------|---------| | mcp>=1.0.0 | Model Context Protocol server framework (FastMCP) | | playwright>=1.40.0 | Browser automation | | python-dotenv>=1.0.0 | Optional env file support |

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Notes

• session.json stores your LinkedIn auth cookies — keep it private, it is excluded from git via .gitignore
• Debug screenshots (*.png) are written to the project folder at runtime and are also gitignored
• Tested on LinkedIn as of June 2026; LinkedIn's DOM can change — if selectors break, check browser.py and update aria-labels or placeholder text