Linkedin Job Assistant

🚀 LinkedIn Model Context Protocol (MCP) Server

A powerful LinkedIn interaction model context protocol server that enables AI assistants to search for jobs, generate resumes and cover letters, and manage job applications.

🚀 Quick Start

This is a robust LinkedIn Model Context Protocol (MCP) Server. It allows AI assistants to perform various LinkedIn - related tasks such as job searching, resume and cover letter generation, and job application management.

✨ Features

• Authentication: Secure LinkedIn authentication and session management.
• Profile Management: Access and update LinkedIn profile information.
• Job Search: Job search with flexible filtering options.
• Resume Generation: Generate customized resumes from LinkedIn profiles.
• Cover Letter Generation: Generate tailored cover letters for specific jobs.
• Job Application: Submit and track job applications.

🔧 Technical Details

This project implements the Model Context Protocol (MCP) specification, allowing AI assistants to interact with LinkedIn through standardized JSON - RPC style requests.

Components:

• MCP Processor: Routes requests to appropriate handlers.
• API Module: A module dedicated to LinkedIn interactions (authentication, job search, profile, etc.).
• Core Protocol: Defines request/response structures and data models.
• Utility Classes: Configuration management and helper functions.

📦 Installation

# Clone the repository
git clone https://github.com/yourusername/linkedin-mcp.git
cd linkedin-mcp

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

📚 Documentation

Configuration

Create a .env file in the project root directory with the following variables:

# LinkedIn credentials
LINKEDIN_USERNAME=your_email@example.com
LINKEDIN_PASSWORD=your_password

# API settings
OPENAI_API_KEY=your_openai_api_key
SESSION_DIR=sessions
DATA_DIR=data

Usage

Start the Server

python server.py

Example MCP Requests

Authentication Example:

{
  "id": "123",
  "method": "linkedin.auth.login",
  "params": {
    "username": "your_username",
    "password": "your_password"
  },
  "jsonrpc": "2.0"
}

Job Search Example:

{
  "id": "456",
  "method": "linkedin.job.search",
  "params": {
    "keywords": "数据分析师",
    "location": "北京"
  },
  "jsonrpc": "2.0"
}

Project Structure

linkedin-mcp/
├── README.md                 # Project documentation
├── requirements.txt         # Dependency management
├── server.py                # Server entry point
├── config.py                # Configuration management
├── utils/                   # Utility classes
│   └── request_handler.py  # Request handler
├── handlers/                # Various handlers
│   ├── auth_handler.py     # Authentication handler
│   └── job_handler.py      # Job handler
└── templates/               # Template files
    ├── cover_letter/
    │   └── standard.html  # Standard cover letter template
    └── resume/
        └── standard.html  # Standard resume template

Running Tests

pytest

📄 License

This project is licensed under the MIT License. Please refer to the LICENSE file for details.

🙏 Acknowledgments

• LinkedIn API documentation
• Model Context Protocol specification