LinkedIn MCP Server

A custom Model Context Protocol server that gives AI assistants control of your LinkedIn account through your own logged-in browser session.

Publish posts, read and edit your profile, search jobs, and apply via Easy Apply — all from a chat conversation with any MCP-compatible client (opencode, Claude, Cursor, and more).

⚠️ Disclaimer: This project automates your real LinkedIn account in a browser. LinkedIn's User Agreement (§8.2) prohibits bots, scraping, and automation. Use at your own risk; aggressive or mass automation can lead to account restriction. All browser actions run locally on your machine.

Quick Start (first-timers)

The 5-minute path from nothing to your first LinkedIn post via AI. Requires only uv, a terminal, and a LinkedIn account.

1. Install uv (skip if you already have it)

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install the package and its browser

uv tool install linkedin-mcp-automation
linkedin-mcp --install-browsers

Alternatively install from source with git clone https://github.com/developer-tusharchauhan/linkedin-mcp.git, uv sync inside the folder, and run via python -m linkedin_mcp.server instead of the linkedin-mcp command.

3. Register with your MCP client

{
  "mcpServers": {
    "linkedin": {
      "command": "uv",
      "args": ["--directory", "YOUR_PATH/linkedin-mcp", "run", "python", "-m", "linkedin_mcp.server"]
    }
  }
}

On Windows, if your client does not resolve linkedin-mcp (e.g. binding to App Control policy), configure command as python -m linkedin_mcp.server after a pip/venv install, or the uv --directory ... python -m form above for a source install.

4. Restart your MCP client, then in the chat:

Run the login tool.

A Chromium window opens — sign in to LinkedIn there (complete 2FA/captcha if asked). After that, session is saved and you can say things like:

• "Read my LinkedIn profile"
• "Publish this post on LinkedIn: I just shipped my first MCP server!"
• "Search for Data Engineer jobs posted this week, remote"
• "Check this job and dry-run the Easy Apply form: "

Seeing Failed to spawn on a Windows machine? Your security policy blocks uv's script shims — the python -m commands above already work around it. Just restart the client.

Features

• Posting — publish posts to your LinkedIn feed
• Profile — read your full profile, edit headline / About, add experience & education entries
• Jobs — search jobs with keyword, location, date, and type filters; fetch full job details
• Easy Apply — dry-run form inspection first, consent-gated submission
• Session persistence — sign in once, reuse the session indefinitely

Tools

Requirements

• uv (Python package manager)
• A LinkedIn account

Installation

Choose one — A is the fastest and requires no Git or source checkout.

Option A — Install from PyPI

# installs the linkedin-mcp command + deps (mcp, patchright)
uv tool install linkedin-mcp-automation

# one-time: download the Chromium browser that drives LinkedIn
linkedin-mcp --install-browsers

Then register the linkedin-mcp command with your MCP client (no path needed):

{ "mcpServers": { "linkedin": { "command": "linkedin-mcp" } } }

Prefer a venv over an isolated tool? uv venv then uv pip install linkedin-mcp-automation, and use python -m linkedin_mcp.server (activate the venv for your MCP client) plus python -m patchright install chromium to set up the browser.

Option B — From source (contributors)

git clone https://github.com/developer-tusharchauhan/linkedin-mcp.git
cd linkedin-mcp

uv sync
uv run patchright install chromium

First run — sign in

The server ships with no credentials. On first use, call the login tool:

1. A Chromium window opens.
2. Sign in to LinkedIn (including 2FA / captcha if prompted).
3. The session is saved to ~/.linkedin-mcp/profile and reused automatically.

Easy Apply auto-fill

Before submitting applications, create ~/.linkedin-mcp/answers.json mapping question labels to your answers. Keys are matched case-insensitively against form labels:

{
  "phone": "+44 7xxx xxx xxxx",
  "city": "London",
  "years of python experience": "3",
  "willing to relocate": "yes"
}

Registering with an MCP client

Installed from PyPI (Option A)? The command is just linkedin-mcp:

{
  "mcpServers": {
    "linkedin": { "command": "linkedin-mcp" }
  }
}

For opencode:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "linkedin": { "type": "local", "command": ["linkedin-mcp"], "enabled": true }
  }
}

Running from source (Option B)? Point at the checkout — works from any directory:

{
  "mcpServers": {
    "linkedin": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/linkedin-mcp", "run", "python", "-m", "linkedin_mcp.server"]
    }
  }
}

Windows / corporate machines: some security policies (App Control / AppLocker) block generated .exe shims (like linkedin-mcp). If you see Failed to spawn, switch to the python -m form — a venv pip install makes python -m linkedin_mcp.server work from any directory — and restart your MCP client.

Releasing a new version

The repo includes a GitHub Actions workflow (.github/workflows/release.yml) that builds and publishes to PyPI automatically when a v* tag is pushed.

1. Create a PyPI account and your project (linkedin-mcp-automation).
2. On the PyPI project page → Publishing → add a Trusted Publisher:
   • GitHub owner: developer-tusharchauhan
   • Repository: linkedin-mcp
   • Workflow name: release.yml
   • Environment: release
3. Tag and push — the workflow builds and publishes for you:

   git tag v0.1.0
   git push origin v0.1.0
   

   (Trigger it manually anytime via Actions → Release to PyPI → Run workflow.)

Manual alternative (one-off): run uv publish locally with UV_PUBLISH_TOKEN set to a PyPI API token.

Example usage

Once connected, just ask your assistant:

• "Publish a post on LinkedIn about my new project"
• "Read my LinkedIn profile"
• "Update my headline to: Software Engineer | AI & Data"
• "Search for Senior Data Engineer jobs posted this week, remote"
• "Check this job and dry-run the Easy Apply form: https://www.linkedin.com/jobs/view/1234567890"

It is recommended to inspect (dry_run) before submitting any application.

How it works

• Patchright (a stealth-patched Playwright fork) drives a persistent Chromium profile.
• A process-wide lock serializes tool calls — one browser at a time.
• LinkedIn selectors are matched against the current DOM; if LinkedIn changes markup, some tools may need a selector refresh.

Project layout

linkedin-mcp/
├── pyproject.toml          # dependencies + entrypoint
├── src/linkedin_mcp/
│   ├── server.py           # MCP server: registers all tools
│   ├── browser.py          # persistent browser session management
│   └── linkedin.py         # automation routines
└── README.md

License

MIT