Iflow Mcp Mcp Linkedin Server

A FastMCP-based server for LinkedIn automation and data extraction using browser automation. This server provides a set of tools for interacting with LinkedIn programmatically while respecting LinkedIn's terms of service and rate limits.

Features

• Secure Authentication

  • Environment-based credential management
  • Session persistence with encrypted cookie storage
  • Rate limiting protection
  • Automatic session recovery

• Profile Operations

  • View and extract profile information
  • Search for profiles based on keywords
  • Browse LinkedIn feed
  • Profile visiting capabilities

• Post Interactions

  • Like posts
  • Comment on posts
  • Read post content and engagement metrics

Prerequisites

• Python 3.8+
• Playwright
• FastMCP library
• LinkedIn account

Installation

1. Clone the repository:

git clone [repository-url]
cd mcp-linkedin-server

2. Create and activate a virtual environment:

python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt
playwright install chromium

4. Set up environment variables: Create a .env file in the root directory with:

[email protected]
LINKEDIN_PASSWORD=your_password
COOKIE_ENCRYPTION_KEY=your_encryption_key  # Optional: will be auto-generated if not provided

Usage

1. Start the MCP server:

python linkedin_browser_mcp.py

2. Available Tools:

• login_linkedin_secure: Securely log in using environment credentials
• browse_linkedin_feed: Browse and extract posts from feed
• search_linkedin_profiles: Search for profiles matching criteria
• view_linkedin_profile: View and extract data from specific profiles
• interact_with_linkedin_post: Like, comment, or read posts

Example Usage

from fastmcp import FastMCP

# Initialize client
client = FastMCP.connect("http://localhost:8000")

# Login
result = await client.login_linkedin_secure()
print(result)

# Search profiles
profiles = await client.search_linkedin_profiles(
    query="software engineer",
    count=5
)
print(profiles)

# View profile
profile_data = await client.view_linkedin_profile(
    profile_url="https://www.linkedin.com/in/username"
)
print(profile_data)

Security Features

• Encrypted cookie storage
• Rate limiting protection
• Secure credential management
• Session persistence
• Browser automation security measures

Best Practices

1. Rate Limiting: The server implements rate limiting to prevent excessive requests:

   • Maximum 5 login attempts per hour
   • Automatic session reuse
   • Cookie persistence to minimize login needs

2. Error Handling: Comprehensive error handling for:

   • Network issues
   • Authentication failures
   • LinkedIn security challenges
   • Invalid URLs or parameters

3. Session Management:

   • Automatic cookie encryption
   • Session persistence
   • Secure storage practices

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT

Disclaimer

This tool is for educational purposes only. Ensure compliance with LinkedIn's terms of service and rate limiting guidelines when using this software.