LinkedIn MCP Server

A production-ready Model Context Protocol (MCP) server for LinkedIn integration with AI-powered content generation and advanced screenshot capabilities. Automate LinkedIn posting, generate engaging content, and capture beautiful screenshots—all through a unified MCP interface.

Features

• 🤖 AI-Powered Content Generation - Generate engaging LinkedIn posts using OpenAI, Google Gemini, or OpenRouter
• 📸 Advanced Screenshot Capture - Capture websites, code snippets, and project demos
• 🔄 Approval Workflow - Preview and approve posts before publishing
• 📊 Analytics - Track post performance and engagement
• 🎨 Image Processing - Create collages, optimize for LinkedIn, and format code beautifully
• 🔐 OAuth 2.0 Authentication - Secure LinkedIn API integration
• 📝 Project Analysis - Automatically analyze projects and generate contextual posts

Installation

Prerequisites

• Bun runtime (latest stable version)
• Linux environment (tested on Ubuntu/Debian)
• Chromium browser for screenshots
• LinkedIn API credentials (see LinkedIn API Setup)

System Dependencies

# Install Chromium and required libraries for Puppeteer
sudo apt-get update
sudo apt-get install -y chromium-browser libgbm1 libasound2

# Optional: Native screenshot tools
sudo apt-get install -y imagemagick

Install Project

# Clone the repository
git clone <your-repo-url>
cd linkedin-mcpserver

# Install dependencies
bun install

# Copy environment template
cp .env.example .env

# Edit .env with your credentials
nano .env

LinkedIn API Setup

1. Create a LinkedIn App

1. Go to LinkedIn Developers
2. Click Create app
3. Fill in app details:
   • App name: Your MCP Server
   • LinkedIn Page: Select or create a page
   • App logo: Upload an icon
   • Privacy policy URL: Your privacy policy
4. Click Create app

2. Get API Credentials

1. Navigate to Auth tab
2. Copy Client ID and Client Secret
3. Add redirect URL: http://localhost:3000/callback
4. Request necessary permissions:
   • w_member_social - Create posts
   • r_liteprofile - Read profile
   • r_organization_social - Organization analytics

3. Get Access Token

There are two methods:

Method A: OAuth Flow (Recommended)

import { LinkedInAuth } from './src/linkedin/auth.js';

const auth = new LinkedInAuth();
const authUrl = auth.getAuthorizationUrl();
console.log('Visit:', authUrl);

// After authorization, exchange code for token
const tokens = await auth.exchangeCodeForToken(code);
// Save tokens to .env

Method B: LinkedIn Token Inspector

1. Go to LinkedIn Token Tools
2. Select your app
3. Select required scopes
4. Click Request access token
5. Copy the access token to .env

4. Configure Environment

Update .env file:

LINKEDIN_CLIENT_ID=your_client_id_here
LINKEDIN_CLIENT_SECRET=your_client_secret_here
LINKEDIN_ACCESS_TOKEN=your_access_token_here
LINKEDIN_REDIRECT_URI=http://localhost:3000/callback

AI_PROVIDER=openai
OPENAI_API_KEY=your_openai_key_here

Configuration

Environment Variables

Variable	Description	Default
LINKEDIN_CLIENT_ID	LinkedIn app client ID	Required
LINKEDIN_CLIENT_SECRET	LinkedIn app client secret	Required
LINKEDIN_ACCESS_TOKEN	LinkedIn access token	Required
LINKEDIN_REFRESH_TOKEN	LinkedIn refresh token	Optional
AI_PROVIDER	AI provider: openai, gemini, openrouter	openai
OPENAI_API_KEY	OpenAI API key	Required if using OpenAI
GEMINI_API_KEY	Google Gemini API key	Required if using Gemini
OPENROUTER_API_KEY	OpenRouter API key	Required if using OpenRouter
AI_MODEL	AI model name	gpt-4
SCREENSHOT_OUTPUT_DIR	Screenshot output directory	./screenshots
SCREENSHOT_DEFAULT_WIDTH	Default viewport width	1920
SCREENSHOT_DEFAULT_HEIGHT	Default viewport height	1080
SCREENSHOT_QUALITY	Image quality (1-100)	90

Usage

Running the Server

# Development mode
bun run dev

# Production mode
bun run start

Using with MCP Clients

VS Code / Cline Configuration

Add to your MCP settings (e.g., ~/.cline/mcp_settings.json):

{
  "mcpServers": {
    "linkedin": {
      "command": "bun",
      "args": ["run", "/path/to/linkedin-mcpserver/src/index.ts"],
      "env": {
        "LINKEDIN_CLIENT_ID": "your_client_id",
        "LINKEDIN_CLIENT_SECRET": "your_client_secret",
        "LINKEDIN_ACCESS_TOKEN": "your_access_token",
        "OPENAI_API_KEY": "your_openai_key"
      }
    }
  }
}

Available Tools

1. analyze_project

Analyze a project and generate LinkedIn post content.

{
  "name": "analyze_project",
  "arguments": {
    "project_path": "./my-project",
    "include_commits": true,
    "commit_count": 5,
    "tone": "professional",
    "length": "medium",
    "capture_screenshots": true,
    "screenshot_config": {
      "include_demo": true,
      "localhost_url": "http://localhost:3000",
      "pages_to_demo": ["/", "/dashboard", "/settings"]
    }
  }
}

2. create_linkedin_post

Create and publish a LinkedIn post.

{
  "name": "create_linkedin_post",
  "arguments": {
    "content": "Excited to share my new project! 🚀\n\nBuilt with React, TypeScript, and Tailwind CSS...",
    "visibility": "PUBLIC",
    "media_urls": ["/path/to/screenshot.png"],
    "require_approval": true
  }
}

3. confirm_post

Confirm or reject a pending post.

{
  "name": "confirm_post",
  "arguments": {
    "request_id": "preview-1234567890",
    "approved": true
  }
}

4. preview_post

Preview a post before publishing.

{
  "name": "preview_post",
  "arguments": {
    "content": "My post content...",
    "visibility": "PUBLIC"
  }
}

5. capture_screenshot

Capture screenshot from URL.

{
  "name": "capture_screenshot",
  "arguments": {
    "source_type": "url",
    "source": "https://example.com",
    "viewport_width": 1920,
    "viewport_height": 1080,
    "full_page": false
  }
}

6. capture_project_demo

Capture multiple pages for project demo.

{
  "name": "capture_project_demo",
  "arguments": {
    "project_url": "http://localhost:3000",
    "pages_to_capture": ["/", "/features", "/pricing"],
    "include_mobile_view": true,
    "create_collage": true
  }
}

7. capture_code_snippet

Capture beautifully formatted code.

{
  "name": "capture_code_snippet",
  "arguments": {
    "file_path": "./src/components/Hero.tsx",
    "line_start": 10,
    "line_end": 30,
    "theme": "github-dark",
    "show_line_numbers": true
  }
}

8. create_screenshot_collage

Combine multiple screenshots.

{
  "name": "create_screenshot_collage",
  "arguments": {
    "screenshot_paths": ["./img1.png", "./img2.png"],
    "layout": "horizontal",
    "spacing": 20
  }
}

9. get_profile_info

Get LinkedIn profile information.

{
  "name": "get_profile_info",
  "arguments": {}
}

10. get_post_analytics

Get post engagement metrics.

{
  "name": "get_post_analytics",
  "arguments": {
    "post_id": "urn:li:share:1234567890"
  }
}

Example Workflows

Example 1: Full Project Announcement

// 1. Analyze project and generate content
const analysis = await analyzedProject({
  project_path: "./my-app",
  capture_screenshots: true,
  screenshot_config: {
    include_demo: true,
    localhost_url: "http://localhost:3000",
    pages_to_demo: ["/", "/dashboard"]
  },
  tone: "professional",
  length: "medium"
});

// 2. Create post with preview
const post = await createLinkedInPost({
  content: analysis.generated_content,
  media_urls: analysis.screenshots,
  visibility: "PUBLIC",
  require_approval: true
});

// 3. Confirm after review
await confirmPost({
  request_id: post.preview_id,
  approved: true
});

Example 2: Code Snippet Share

// 1. Capture code screenshot
const code = await captureCodeSnippet({
  file_path: "./src/auth/oauth.ts",
  line_start: 45,
  line_end: 75,
  theme: "github-dark"
});

// 2. Post with manual content
await createLinkedInPost({
  content: "Just implemented OAuth 2.0 authentication! 🔐\n\nHere's the core logic...",
  media_urls: [code.path],
  require_approval: false
});

Example 3: Weekly Progress Update

// Analyze recent commits
const analysis = await analyzeProject({
  include_commits: true,
  commit_count: 20,
  tone: "casual",
  length: "short"
});

// Post update
await createLinkedInPost({
  content: analysis.generated_content,
  visibility: "PUBLIC"
});

Troubleshooting

Authentication Issues

Error: "Invalid access token"

• Verify token is correct in .env
• Check if token has expired (LinkedIn tokens expire)
• Request a new token from LinkedIn Developer Portal

Error: "Insufficient permissions"

• Verify your app has required scopes: w_member_social, r_liteprofile
• Re-authorize with correct permissions

Screenshot Issues

Error: "Failed to launch browser"

# Install Chromium
sudo apt-get install chromium-browser

# If on WSL, you may need:
export PUPPETEER_SKIP_CHROMIUM_DOWNLOAD=true
export PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser

Error: "Page load timeout"

• Increase timeout in code or use delay_ms parameter
• Check if localhost server is running
• Verify URL is accessible

API Rate Limits

LinkedIn has rate limits:

• Posts: 100 per day
• API calls: Varies by endpoint

The server implements rate limiting and will throw RateLimitError when exceeded.

Memory Issues

If browser instances aren't being cleaned up:

// The server automatically cleans up, but you can force cleanup
import { puppeteerService } from './src/screenshots/puppeteer-service.js';
await puppeteerService.cleanup();

Development

Project Structure

linkedin-mcpserver/
├── src/
│   ├── index.ts                 # MCP server entry point
│   ├── config.ts               # Configuration management
│   ├── linkedin/               # LinkedIn API integration
│   │   ├── auth.ts
│   │   ├── client.ts
│   │   ├── posts.ts
│   │   ├── media.ts
│   │   └── types.ts
│   ├── project-analysis/       # Project analysis & AI
│   │   ├── file-reader.ts
│   │   ├── git-analyzer.ts
│   │   ├── content-generator.ts
│   │   ├── context-builder.ts
│   │   └── ai-providers/
│   ├── screenshots/            # Screenshot system
│   │   ├── capturer.ts
│   │   ├── puppeteer-service.ts
│   │   ├── code-formatter.ts
│   │   ├── image-processor.ts
│   │   └── types.ts
│   ├── tools/                  # MCP tools
│   │   ├── analyze-project.ts
│   │   ├── create-post.ts
│   │   ├── capture-screenshot.ts
│   │   └── ...
│   └── utils/                  # Utilities
│       ├── logger.ts
│       ├── error-handler.ts
│       └── rate-limiter.ts
├── .env.example
├── package.json
├── tsconfig.json
└── README.md

Running Tests

bun test

Building

bun run build

Security Considerations

• Never commit .env file with credentials
• Validate all inputs before posting to LinkedIn
• Be careful with screenshots - don't capture sensitive data
• Use approval workflow for important posts
• Monitor API usage to avoid rate limits
• Review generated content before posting

Rate Limits

LinkedIn API limits:

• Posts: 100 per day
• Profile reads: More lenient
• Analytics: Limited per hour

The server implements rate limiting to prevent exceeding these limits.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details

Support

For issues and questions:

• GitHub Issues: [Your repo issues page]
• Documentation: This README
• LinkedIn API Docs: https://docs.microsoft.com/en-us/linkedin/

Acknowledgments

• Built with Model Context Protocol
• Uses Puppeteer for screenshots
• Powered by Bun runtime
• AI providers: OpenAI, Google Gemini, OpenRouter

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Happy posting! 🚀