LinkedIn MCP Pro Max

A high-performance, autonomous Model Context Protocol (MCP) server that turns LinkedIn into an API for your AI workflows. Built with Clean Architecture, stealth browser automation, and a convention-based zero-config component registry.

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Quick Start

1. Prerequisites

Ensure you have uv installed:

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

2. Installation & Setup

Method A: Automated Setup (Recommended)

chmod +x scripts/setup.sh
./scripts/setup.sh

The script handles dependency syncing, .env bootstrapping, and stealth browser provisioning.

Method B: Manual Setup

uv sync
uv run python -m patchright install chromium
cp .env.example .env

Edit .env with your LinkedIn credentials:

LINKEDIN_EMAIL="[email protected]"
LINKEDIN_PASSWORD="your-secure-password"
LINKEDIN_USERNAME="your-profile-slug"

4. First-Run Authentication

uv run linkedin-mcp-pro-max --login

5. Connect to Claude Desktop (or any MCP client)

Add to your claude_desktop_config.json:

{
    "mcpServers": {
        "linkedin-mcp-pro-max": {
            "command": "/home/naimul/.local/bin/uv",
            "args": [
                "--directory",
                "/home/naimul/linkedin-mcp-pro-max",
                "run",
                "linkedin-mcp-pro-max"
            ]
        }
    }
}

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The MCP Toolkit (14 Unified Tools)

Category	Tool	Actions	Description
Profile	profile	get, analyze, update, update_cover_image	Manage deep profile data, AI analysis, and identity updates
	experience	add, update, delete	Manage professional experience entries
	education	add, update, delete	Manage education entries
	skills	add, delete	Manage skills on your profile
	company	-	Get detailed corporate metadata and insights
Jobs & Intel	job	search, details, recommended, apply	Discover, analyze, and apply for job postings
	application	list, track, update	Manage internal job application tracking
Content	create_linkedin_post	-	Publish AI-generated posts autonomously
	interact_with_post	read, like, comment	Engage with feed posts via URL
Documents	generate_resume	-	Generate a professional resume from your profile
	tailor_resume	-	Target your resume to match a specific Job ID
	generate_cover_letter	-	Create a personalized contextual cover letter
	list_templates	-	View all available document templates
System	server	restart	Manage the MCP server lifecycle

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Architecture

Built on Clean Architecture with a one-way dependency rule and a Unified Component Registry that eliminates all manual wiring.

[tools/]  →  [services/]  →  [browser/actors/ + browser/scrapers/]
              ctx.my_svc        manager.my_actor / manager.my_scraper

Directory Structure

src/
├── app.py                  # Composition root — auto-wires from registry
├── helpers/
│   └── registry.py         # Unified discovery engine (ServiceMeta, ActorMeta, ScraperMeta)
├── tools/                  # MCP tool definitions (@mcp.tool) — auto-discovered
├── services/               # Business logic layer — auto-wired via SERVICE markers
├── browser/
│   ├── actors/             # Write operations (UI interaction) — auto-registered
│   ├── scrapers/           # Read operations (data extraction) — auto-registered
│   ├── manager.py          # Orchestrator — auto-instantiates actors/scrapers
│   └── helpers/            # Low-level browser utilities (driver, sniffer, dom)
├── api/                    # LinkedIn internal API client
├── db/                     # Database repositories
├── schema/                 # Pydantic domain models
├── config/                 # Settings and environment
└── providers/              # AI provider wrappers (OpenAI, Claude)

The Zero-Config Flow

At startup, helpers/registry.py scans services/, browser/actors/, and browser/scrapers/ automatically:

discover_all()
├── services/*.py     → SERVICE = ServiceMeta(...)   → injected into AppContext
├── browser/actors/*  → ACTOR   = ActorMeta(...)     → instantiated in BrowserManager
└── browser/scrapers/ → SCRAPER = ScraperMeta(...)   → instantiated in BrowserManager

No manual registration. No editing app.py or manager.py.

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Adding New Features

For the complete development pipeline, debugging guide, and working examples, see the Tool Development Guide.

A full feature (scraper + service + tool) requires exactly 3 new files. No existing file is modified.

1. Browser Scraper — src/browser/scrapers/my_feature.py

from helpers.registry import ScraperMeta

class MyFeatureScraper:
    def __init__(self, page): ...
    async def scrape(self): ...

SCRAPER = ScraperMeta(attr="my_feature_scraper", cls=MyFeatureScraper)

2. Service — src/services/my_feature.py

from helpers.registry import ServiceMeta

class MyFeatureService:
    def __init__(self, browser=None): ...
    async def do_work(self): ...

SERVICE = ServiceMeta(attr="my_feature", cls=MyFeatureService, deps=["browser"], lazy=True)

3. Tool — src/tools/my_feature.py

from app import mcp, get_ctx

@mcp.tool()
async def my_feature_tool(param: str) -> str:
    """Description the AI reads to decide when to use this tool."""
    ctx = await get_ctx()
    await ctx.initialize_browser()
    result = await ctx.my_feature.do_work()
    return json.dumps(result)

app.py, manager.py, services/__init__.py, tools/__init__.py — never touched.

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uv run linkedin-mcp-pro-max             # Start MCP server
uv run linkedin-mcp-pro-max --login     # Autonomous headless login
uv run linkedin-mcp-pro-max --status    # Check authentication status
uv run linkedin-mcp-pro-max --logout    # Clear session and cookies

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Documentation

Document	Description
Tool Development Guide	Full pipeline: creating tools, services, actors, scrapers. Debugging guide.
Services README	Service layer conventions and dependency rules
Actors README	Actor conventions and browser interaction patterns
Schema README	Pydantic model conventions

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Automating your professional identity smartly, securely, and seamlessly.
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