LinkedIn MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LinkedIn MCP Serversearch for data science internships in New York"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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
Related MCP server: BossZhipin MCP Server
Setup
1. Install dependencies
pip install -r requirements.txt
playwright install chromium2. 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 activeThe 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.pyProject Structure
linkedin_mcp/
├── server.py # FastMCP server — all tools + resume/profile data
├── browser.py # Playwright automation class (LinkedInBrowser)
├── requirements.txt # Python dependencies
├── .gitignore
└── README.mdTools Reference
Tool | Description |
| Check if LinkedIn session is still active |
| Open browser for manual login and save session |
| Scrape current profile (name, headline, about, experience, education, skills) |
| Compare profile against resume data and return suggestions |
| Update all experience entries with titles, dates, and descriptions |
| Update education entries with GPA and descriptions |
| Update all project entries with names and descriptions |
| Update headline and about section |
| Search jobs by keyword, location, date filter, and job type |
| Search DS/ML/AI internships posted in the last week |
| Search entry-level DS/ML full-time roles |
| Fetch full job description from a listing URL |
Key Technical Notes
React inputs — uses Playwright
.fill(), not JS.valueassignment (JS bypasses React state)Obfuscated field IDs — field IDs are dynamically resolved by finding label elements by text, then reading their
htmlForattributeLinkedIn edit URLs — discovered dynamically from
<a>tags on details pages; edit form renders immediately when navigated to directlySPA routing — LinkedIn uses
history.pushState; pollspage.urlinstead of waiting for Playwright navigation eventsExperience description —
contenteditablediv filled viaexecCommand('insertText'); education and project descriptions are regular<textarea>elements
Dependencies
Package | Purpose |
| Model Context Protocol server framework (FastMCP) |
| Browser automation |
| Optional env file support |
Notes
session.jsonstores your LinkedIn auth cookies — keep it private, it is excluded from git via.gitignoreDebug screenshots (
*.png) are written to the project folder at runtime and are also gitignoredTested on LinkedIn as of June 2026; LinkedIn's DOM can change — if selectors break, check
browser.pyand update aria-labels or placeholder text
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