# mcp-content-pipeline

[![PyPI version](https://img.shields.io/pypi/v/mcp-content-pipeline)](https://pypi.org/project/mcp-content-pipeline/)
[![Downloads](https://img.shields.io/pypi/dm/mcp-content-pipeline)](https://pypi.org/project/mcp-content-pipeline/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python](https://img.shields.io/pypi/pyversions/mcp-content-pipeline)](https://pypi.org/project/mcp-content-pipeline/)

A YouTube video analysis and content generation pipeline exposed as [MCP](https://modelcontextprotocol.io/) 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

```bash
uvx mcp-content-pipeline
```

Or install explicitly:

```bash
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`):

```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

```bash
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](https://www.conventionalcommits.org/) (`feat: add new feature`)
4. Push and open a Pull Request

## License

[MIT](LICENSE)
