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LinkedIn Model Context Protocol (MCP) Server

by Rayyan9477
GitHub
Python
10
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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

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

Installation

  1. Clone the repository

    git clone https://github.com/yourusername/linkedin-mcp.git cd linkedin-mcp

  2. Create and activate a virtual environment

    # Linux/macOS python -m venv venv source venv/bin/activate # Windows python -m venv venv .\venv\Scripts\activate

  3. Install dependencies

    pip install -r requirements.txt

  4. 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
F
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.

  1. Features
    1. Architecture
      1. Project Structure
    2. Getting Started
      1. Prerequisites
      2. Installation
    3. Configuration
      1. Usage
        1. Starting the Server
        2. Example MCP Requests
      2. Available Methods
        1. Development
          1. Project Structure
          2. Running Tests
        2. License
          1. Acknowledgments

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