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2025-02-23

MCP编码DEVOPS

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🎬 Encoding DevOps MCP Server: AI-Powered Video Encoding Assistant

GitHub stars GitHub contributors GitHub last commit open issues Python Version PRs Welcome

Ever been woken up at 3 AM by a failed encoding job? Say goodbye to those late-night troubleshooting sessions! This Model Context Protocol (MCP) server connects Anthropic's Claude directly to your encoding workflow, making video encoding issues a breeze to handle.

✨ What's Cool About This?

  • Smart Error Translation: Turns cryptic "moov atom not found" messages into plain English
  • Real-time Analysis: Connects directly to your encoding workflow and database
  • Human-Friendly Responses: Generates clear, actionable solutions for your team
  • Auto-Email Draft: Creates professional client communications with context
  • Always On Guard: Monitors your encoding jobs 24/7
  • Keeps You in Control: Suggests actions but lets you make the final call

🚀 Getting Started

You'll Need

  • Python 3.11 or higher
  • Claude Desktop
  • Your encoding workflow API credentials
  • OMDB API key (optional, for movie metadata)

Quick Setup

  1. Install the package using UV:
curl -LsSf https://astral.sh/uv/install.sh | sh
uv pip install encoding-devops
  1. Set up your environment:
# Copy the example config
cp .env.example .env

# Add your API keys
nano .env
  1. Register with Claude Desktop:
uv run mcp install ./src/encoding_devops/main.py

💡 How to Use It

# Start the MCP server
uv run mcp dev ./src/encoding_devops/main.py

# In Claude Desktop, you can now ask things like:
"What's wrong with job XYZ-123?"
"Draft an email about the failed encoding job"
"Check the encoding cluster status"

🔧 Under the Hood

The MCP server uses three main components to help you:

  1. Resources: Email templates, error guides, and documentation
  2. Tools: Job status checks, log analysis, and email drafting
  3. Prompts: Instructions that help Claude understand encoding issues

🤝 Want to Help?

We'd love your input! Here's how you can contribute:

  1. Fork it
  2. Create your feature branch (git checkout -b feature/awesome-feature)
  3. Commit your changes (git commit -m 'Add awesome feature')
  4. Push to the branch (git push origin feature/awesome-feature)
  5. Open a Pull Request

📋 Coming Soon

  • Integration with more encoding workflow systems
  • Advanced log analysis patterns
  • Automated health checks
  • Slack notifications
  • Custom email templates

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙌 Thanks To

  • Anthropic team for the MCP framework
  • All our contributors
  • The DevOps community for feedback and suggestions

💤 Built by a developer who wanted to sleep through the night. If this helps you too, give us a star!

Read the full story behind this project in my Medium article about using MCP to handle encoding fires.

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    Reviews

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    user_bTFIxQZA
    2025-04-16

    The Crawl4AI MCP Server designed by seungwonme is an exceptional tool for AI enthusiasts. Its user-friendly interface and powerful capabilities make web crawling a seamless experience. The server's efficiency and comprehensive features greatly enhance data gathering processes, proving to be indispensable for any AI project. Highly recommended!