LinkedIn Model Context Protocol (MCP) Server

A powerful Model Context Protocol server for LinkedIn interactions that enables AI assistants to search for jobs, generate resumes and cover letters, and manage job applications programmatically.

Features

Authentication: Secure OAuth 2.0 authentication with token refresh
Profile Management: Access and update LinkedIn profile information
Job Search: Advanced job search with filtering and pagination
Resume & Cover Letters: Generate tailored resumes and cover letters
Messaging: Send messages and connection requests
Analytics: Track job applications and engagement metrics
Async API: Built with asyncio for high performance
Modular Design: Clean, maintainable code with separation of concerns

Architecture

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

Project Structure

linkedin_mcp/
├── api/
│   ├── clients/         # API client implementations
│   │   ├── __init__.py   # Client factory functions
│   │   ├── linkedin.py   # LinkedIn API client
│   │   └── openai.py     # OpenAI integration
│   │
│   ├── models/          # Data models and schemas
│   │   ├── __init__.py   # Model exports
│   │   ├── common.py     # Common data models
│   │   ├── enums.py      # Enumerations
│   │   ├── requests.py   # Request models
│   │   └── responses.py  # Response models
│   │
│   └── services/        # Business logic
│       └── ...
│
├── core/                # Core application logic
│   ├── __init__.py
│   ├── exceptions.py    # Custom exceptions
│   ├── mcp_handler.py   # MCP protocol handler
│   └── protocol.py      # Protocol definitions
│
├── utils/              # Utility functions
│   ├── __init__.py
│   ├── auth.py          # Authentication helpers
│   ├── rate_limiter.py  # Rate limiting
│   └── retry.py         # Retry mechanisms
│
├── examples/           # Example scripts
│   └── basic_usage.py   # Basic client usage example
│
├── .env.example       # Example environment variables
├── README.md           # This file
└── requirements.txt    # Project dependencies

Getting Started

Prerequisites

Python 3.8+
LinkedIn Developer Account
OAuth 2.0 credentials from LinkedIn Developers

Installation

Clone the repository
git clone https://github.com/yourusername/linkedin-mcp.git cd linkedin-mcp
Create and activate a virtual environment
# Linux/macOS python -m venv venv source venv/bin/activate # Windows python -m venv venv .\venv\Scripts\activate
Install dependencies
pip install -r requirements.txt
Set up environment variables
cp .env.example .env
Edit the .env file with your LinkedIn API credentials and other settings.

Configuration

Create a .env file in the project root with the following variables (see .env.example for details):

# LinkedIn API Credentials (required)
LINKEDIN_CLIENT_ID=your_client_id_here
LINKEDIN_CLIENT_SECRET=your_client_secret_here
LINKEDIN_REDIRECT_URI=http://localhost:8080/callback

# Optional: OpenAI API Key (for resume/cover letter generation)
# OPENAI_API_KEY=your_openai_api_key_here

# Optional: Logging
LOG_LEVEL=INFO

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

Usage

Starting the Server

python server.py

Example MCP Requests

Authentication

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "linkedin.login",
  "params": {
    "username": "user@example.com",
    "password": "password123"
  }
}

Searching for Jobs

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "linkedin.searchJobs",
  "params": {
    "filter": {
      "keywords": "software engineer",
      "location": "New York, NY",
      "distance": 25
    },
    "page": 1,
    "count": 20
  }
}

Generating a Resume

{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "linkedin.generateResume",
  "params": {
    "profileId": "user123",
    "template": "standard",
    "format": "pdf"
  }
}

Available Methods

Method	Description
`linkedin.login`	Authenticate with LinkedIn
`linkedin.logout`	End the current session
`linkedin.checkSession`	Check if the current session is valid
`linkedin.getFeed`	Get LinkedIn feed posts
`linkedin.getProfile`	Get LinkedIn profile information
`linkedin.getCompany`	Get company profile information
`linkedin.searchJobs`	Search for jobs with filters
`linkedin.getJobDetails`	Get detailed information about a job
`linkedin.getRecommendedJobs`	Get job recommendations
`linkedin.generateResume`	Generate a resume from a LinkedIn profile
`linkedin.generateCoverLetter`	Generate a cover letter for a job application
`linkedin.tailorResume`	Customize a resume for a specific job
`linkedin.applyToJob`	Apply to a job
`linkedin.getApplicationStatus`	Check application status
`linkedin.getSavedJobs`	Get saved jobs
`linkedin.saveJob`	Save a job for later

Development

Project Structure

linkedin-mcp/
├── README.md
├── requirements.txt
├── server.py
├── data/
│   ├── applications/
│   ├── companies/
│   ├── cover_letters/
│   ├── jobs/
│   ├── profiles/
│   └── resumes/
├── linkedin_mcp/
│   ├── api/
│   │   ├── auth.py
│   │   ├── cover_letter_generator.py
│   │   ├── job_application.py
│   │   ├── job_search.py
│   │   ├── profile.py
│   │   └── resume_generator.py
│   ├── core/
│   │   ├── mcp_handler.py
│   │   └── protocol.py
│   └── utils/
│       └── config.py
├── sessions/
└── templates/
    ├── cover_letter/
    │   └── standard.html
    └── resume/
        └── standard.html

Running Tests

pytest

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

LinkedIn API documentation
Model Context Protocol specification

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A server that enables AI assistants to interact with LinkedIn programmatically for job searching, resume/cover letter generation, and managing job applications through standardized JSON-RPC requests.

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