YouTube to LinkedIn MCP Server

LinkedIn Post Generator

A Model Context Protocol (MCP) server that automates generating professional LinkedIn post drafts from YouTube videos. This tool streamlines content repurposing by extracting transcripts from YouTube videos, summarizing the content, and generating engaging LinkedIn posts tailored to your preferences.

Table of Contents

• Features
• Installation
  • Local Development
  • Using with Claude Desktop
• Configuration
• Usage
  • Available Tools
  • Workflow Example
• Deployment
• License

Features

• YouTube Transcript Extraction: Automatically extract transcripts from any YouTube video
• Content Summarization: Generate concise summaries with customizable tone and target audience
• LinkedIn Post Generation: Create professional LinkedIn posts with customizable style and tone
• All-in-One Workflow: Go from YouTube URL to LinkedIn post in a single operation
• Customization Options: Adjust tone, audience, word count, and more to match your personal brand
• MCP Integration: Works seamlessly with AI assistants that support the Model Context Protocol

Installation

Local Development

1. Clone the repository:

   git clone https://github.com/NvkAnirudh/LinkedIn-Post-Generator.git
   cd LinkedIn-Post-Generator
   

2. Install dependencies:

   npm install
   

3. Create a .env file based on the example:

   cp .env.example .env
   

4. Add your API keys to the .env file:

   OPENAI_API_KEY=your_openai_api_key
   YOUTUBE_API_KEY=your_youtube_api_key
   

5. Run the server:

   npm run dev
   

6. Test with MCP Inspector:

   npm run inspect
   

Using with Claude Desktop

This MCP server is designed to work with Claude Desktop and other AI assistants that support the Model Context Protocol. To use it with Claude Desktop:

1. Install the LinkedIn Post Generator MCP server from Smithery:

   npx -y @smithery/cli install yt-to-linkedin-mcp --client claude
   

2. Restart Claude Desktop

3. In Claude Desktop, you can now access the LinkedIn Post Generator tools

Configuration

The application requires API keys to function properly:

1. OpenAI API Key (required): Used for content summarization and post generation
2. YouTube API Key (optional): Enhances YouTube metadata retrieval

You can provide these keys in two ways:

• As environment variables in a .env file
• Directly through the MCP interface using the set_api_keys tool

Usage

Available Tools

Set API Keys

• Tool: set_api_keys
• Purpose: Configure your API keys
• Parameters:
  • openaiApiKey: Your OpenAI API key (required)
  • youtubeApiKey: Your YouTube API key (optional)

Check API Keys

• Tool: check_api_keys
• Purpose: Verify your API key configuration status

Extract Transcript

• Tool: extract_transcript
• Purpose: Get the transcript from a YouTube video
• Parameters:
  • youtubeUrl: URL of the YouTube video

Summarize Transcript

• Tool: summarize_transcript
• Purpose: Create a concise summary of the video content
• Parameters:
  • transcript: The video transcript text
  • tone: Educational, inspirational, professional, or conversational
  • audience: General, technical, business, or academic
  • wordCount: Approximate word count for the summary (100-300)

Generate LinkedIn Post

• Tool: generate_linkedin_post
• Purpose: Create a LinkedIn post from a summary
• Parameters:
  • summary: Summary of the video content
  • videoTitle: Title of the YouTube video
  • speakerName: Name of the speaker (optional)
  • hashtags: Relevant hashtags (optional)
  • tone: First-person, third-person, or thought-leader
  • includeCallToAction: Whether to include a call to action

All-in-One: YouTube to LinkedIn Post

• Tool: youtube_to_linkedin_post
• Purpose: Complete workflow from YouTube URL to LinkedIn post
• Parameters:
  • youtubeUrl: YouTube video URL
  • tone: Desired tone for the post
  • Plus additional customization options

Workflow Example

1. Set your API keys using the set_api_keys tool
2. Use the youtube_to_linkedin_post tool with a YouTube URL
3. Receive a complete LinkedIn post draft ready to publish

Deployment

This server is deployed on Smithery, a platform for hosting and sharing MCP servers. The deployment configuration is defined in the smithery.yaml file.

To deploy your own instance:

1. Create an account on Smithery
2. Install the Smithery CLI:

   npm install -g @smithery/cli
   

3. Deploy the server:

   smithery deploy
   

License

MIT