mcp-content-pipeline 0.5.1

YouTube video analysis and content generation pipeline exposed as MCP tools

mcp-content-pipeline

A YouTube video analysis and content generation pipeline exposed as MCP tools. Extract transcripts, generate key takeaways, TLDRs, and Twitter/X hook drafts — all callable by any MCP-compatible AI client like Claude Desktop.

Why?

Manually copying YouTube transcripts into AI tools, crafting prompts, and formatting output is tedious and error-prone. This MCP server turns the entire workflow into chainable tools that any AI agent can call. List videos from a channel, analyse them in batch, and sync the results to GitHub — all in a single conversation.

Quick Start

uvx mcp-content-pipeline

Or install explicitly:

uv tool install mcp-content-pipeline
mcp-content-pipeline

Claude Desktop Configuration

Add to your Claude Desktop MCP config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "content-pipeline": {
      "command": "/usr/local/bin/uvx",
      "args": ["mcp-content-pipeline"],
      "env": {
        "MCP_CP_ANTHROPIC_API_KEY": "sk-ant-...",
        "MCP_CP_GITHUB_TOKEN": "ghp_...",
        "MCP_CP_GITHUB_REPO": "your-username/your-repo",
        "MCP_CP_GEMINI_API_KEY": "your-gemini-api-key"
      }
    }
  }
}

Tools

Tool	Description	Requires
analyse_video	Analyse a single YouTube video — transcript, takeaways, TLDR, Twitter hook	ANTHROPIC_API_KEY
batch_analyse	Analyse multiple videos from a URL list or config file	ANTHROPIC_API_KEY
list_channel_videos	Fetch recent videos from a YouTube channel	YOUTUBE_API_KEY
sync_to_github	Push analyses as markdown files to a GitHub repo	GITHUB_TOKEN, GITHUB_REPO
generate_image	Generate comic-book infographic from analysis result	GEMINI_API_KEY

Environment Variables

All prefixed with MCP_CP_:

Variable	Required	Description
MCP_CP_ANTHROPIC_API_KEY	Yes	Anthropic API key for Claude analysis
MCP_CP_YOUTUBE_API_KEY	No	YouTube Data API v3 key (only for list_channel_videos)
MCP_CP_GITHUB_TOKEN	For sync	GitHub personal access token
MCP_CP_GITHUB_REPO	For sync	Target repo in owner/repo format
MCP_CP_GITHUB_BRANCH	No	Branch to push to (default: main)
MCP_CP_GITHUB_OUTPUT_DIR	No	Output directory in repo (default: content/videos)
MCP_CP_CLAUDE_MODEL	No	Claude model to use (default: claude-sonnet-4-20250514)
MCP_CP_MAX_TRANSCRIPT_TOKENS	No	Max transcript length in tokens (default: 100000)
MCP_CP_GEMINI_API_KEY	For image	Google AI Studio API key for image generation
MCP_CP_GEMINI_MODEL	No	Gemini model for images (default: gemini-2.5-flash-image)

Example Workflow

Chain tools together in a single conversation:

1. "List the last 5 videos from channel UC_x5XG1OV2P6uZZ5FSM9Ttw"
   → list_channel_videos returns 5 video URLs

2. "Analyse all of these videos"
   → batch_analyse processes all 5, returns analyses

3. "Generate images for each analysis"
   → generate_image creates comic-book infographics for each

4. "Sync the results to GitHub"
   → sync_to_github pushes markdown files + images + index to your repo

Development

git clone https://github.com/your-username/mcp-content-pipeline.git
cd mcp-content-pipeline
uv sync
uv run pytest -v --cov=src/mcp_content_pipeline
uv run ruff check src/ tests/

Security

• All credentials are configured via local environment variables — never committed to the repo
• The tool is open source but your API keys, YouTube key, and GitHub token stay on your machine
• Never create a .env file in the repo — use shell exports or Claude Desktop config instead

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feat/my-feature)
3. Commit using Conventional Commits (feat: add new feature)
4. Push and open a Pull Request

License

MIT