remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Integrations

Leverages OpenAI GPT models to summarize video transcripts and generate professional LinkedIn post content with customizable tone, voice, and audience targeting.
Extracts transcripts from YouTube videos to be used for content generation, supporting multiple languages and retrieving video metadata like title and channel name.

YouTube to LinkedIn MCP Server

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Features

YouTube Transcript Extraction: Extract transcripts from YouTube videos using video URLs
Transcript Summarization: Generate concise summaries of video content using OpenAI GPT
LinkedIn Post Generation: Create professional LinkedIn post drafts with customizable tone and style
Modular API Design: Clean FastAPI implementation with well-defined endpoints
Containerized Deployment: Ready for deployment on Smithery

Setup Instructions

Prerequisites

Python 3.8+
Docker (for containerized deployment)
OpenAI API Key
YouTube Data API Key (optional, but recommended for better metadata)

Local Development

Clone the repository:
git clone <repository-url> cd yt-to-linkedin
Create a virtual environment and install dependencies:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt
Create a .env file in the project root with your API keys:
OPENAI_API_KEY=your_openai_api_key YOUTUBE_API_KEY=your_youtube_api_key
Run the application:
uvicorn app.main:app --reload
Access the API documentation at http://localhost:8000/docs

Docker Deployment

Build the Docker image:
docker build -t yt-to-linkedin-mcp .
Run the container:
docker run -p 8000:8000 --env-file .env yt-to-linkedin-mcp

Smithery Deployment

Ensure you have the Smithery CLI installed and configured.
Deploy to Smithery:
smithery deploy

API Endpoints

1. Transcript Extraction

Endpoint: /api/v1/transcript
Method: POST
Description: Extract transcript from a YouTube video

Request Body:

{
  "youtube_url": "https://www.youtube.com/watch?v=VIDEO_ID",
  "language": "en",
  "youtube_api_key": "your_youtube_api_key"  // Optional, provide your own YouTube API key
}

Response:

{
  "video_id": "VIDEO_ID",
  "video_title": "Video Title",
  "transcript": "Full transcript text...",
  "language": "en",
  "duration_seconds": 600,
  "channel_name": "Channel Name",
  "error": null
}

2. Transcript Summarization

Endpoint: /api/v1/summarize
Method: POST
Description: Generate a summary from a video transcript

Request Body:

{
  "transcript": "Video transcript text...",
  "video_title": "Video Title",
  "tone": "professional",
  "audience": "general",
  "max_length": 250,
  "min_length": 150,
  "openai_api_key": "your_openai_api_key"  // Optional, provide your own OpenAI API key
}

Response:

{
  "summary": "Generated summary text...",
  "word_count": 200,
  "key_points": [
    "Key point 1",
    "Key point 2",
    "Key point 3"
  ]
}

3. LinkedIn Post Generation

Endpoint: /api/v1/generate-post
Method: POST
Description: Generate a LinkedIn post from a video summary

Request Body:

{
  "summary": "Video summary text...",
  "video_title": "Video Title",
  "video_url": "https://www.youtube.com/watch?v=VIDEO_ID",
  "speaker_name": "Speaker Name",
  "hashtags": ["ai", "machinelearning"],
  "tone": "professional",
  "voice": "first_person",
  "audience": "technical",
  "include_call_to_action": true,
  "max_length": 1200,
  "openai_api_key": "your_openai_api_key"  // Optional, provide your own OpenAI API key
}

Response:

{
  "post_content": "Generated LinkedIn post content...",
  "character_count": 800,
  "estimated_read_time": "About 1 minute",
  "hashtags_used": ["#ai", "#machinelearning"]
}

4. Output Formatting

Endpoint: /api/v1/output
Method: POST
Description: Format the LinkedIn post for output

Request Body:

{
  "post_content": "LinkedIn post content...",
  "format": "json"
}

Response:

{
  "content": {
    "post_content": "LinkedIn post content...",
    "character_count": 800
  },
  "format": "json"
}

Environment Variables

Variable	Description	Required
OPENAI_API_KEY	OpenAI API key for summarization and post generation	No (can be provided in requests)
YOUTUBE_API_KEY	YouTube Data API key for fetching video metadata	No (can be provided in requests)
PORT	Port to run the server on (default: 8000)	No

Note: While environment variables for API keys are optional (as they can be provided in each request), it's recommended to set them for local development and testing. When deploying to Smithery, users will need to provide their own API keys in the requests.

License

MIT

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Integrations

YouTube to LinkedIn MCP Server

Features

Setup Instructions

Prerequisites

Local Development

Docker Deployment

Smithery Deployment

API Endpoints

1. Transcript Extraction

2. Transcript Summarization

3. LinkedIn Post Generation

4. Output Formatting

Environment Variables

License

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