LinkedIn Model Context Protocol (MCP) Server

License: MIT Python Version Code Style: Black

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

Related MCP server: LinkedIn MCP Server

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

LinkedIn Model Context Protocol (MCP) Server

LinkedIn Model Context Protocol (MCP) Server

Features

Architecture

Project Structure

Getting Started

Prerequisites

Installation

Configuration

Usage

Starting the Server

Example MCP Requests

Authentication

Searching for Jobs

Generating a Resume

Available Methods

Development

Project Structure

Running Tests

License

Acknowledgments

Resources

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