What is this?

mega-mcp is a Model Context Protocol (MCP) server that exposes a public MEGA folder or file link as a queryable knowledge base. Point it at a share link and your AI assistant — Claude Code, Claude Desktop, or any MCP-compatible client — can browse the tree, search filenames, read text files and extract text from PDFs, inspect video/audio metadata, and optionally transcribe media to text.

No MEGA login. No re-uploading your files somewhere else. Just a link.

Great for course libraries, research archives, documentation dumps, datasets, meeting recordings — any pile of files already living on MEGA that you want an LLM to actually use.

✨ Features

• 🔓 Public links only — folder links and single-file links, zero credentials.
• 📦 Batteries included — ffmpeg/ffprobe ship via npm; PDF extraction uses a bundled pdf.js. No system installs.
• 🧩 Pluggable transcription — bring OpenAI Whisper, a local model, or any command. Off by default.
• ⏱️ Long-job friendly — emits MCP progress so big transcriptions don't hit client timeouts.
• 🛡️ Safe by default — size caps, binary detection, and friendly errors for dead/blocked/expired links.

📑 Table of contents

• Quick start
• Use with Claude Code
• Use with Claude Desktop
• Configuration
• Transcription (optional)
• Tool reference & example prompts
• How it works
• FAQ
• Roadmap
• Contributing
• License

🚀 Quick start

git clone https://github.com/Anicodeth/mega-mcp.git
cd mega-mcp
npm install
npm run build

That's it — dist/index.js is your MCP server. Wire it into a client below.

🤖 Use with Claude Code

claude mcp add mega \
  -e MEGA_LINK="https://mega.nz/folder/XXXX#YYYY" \
  -- node "/absolute/path/to/mega-mcp/dist/index.js"

Then just ask: "Browse my MEGA folder and summarize what's in the Strategy section."

On Windows, use the full path, e.g. node "C:\\path\\to\\mega-mcp\\dist\\index.js".

🖥️ Use with Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "mega": {
      "command": "node",
      "args": ["/absolute/path/to/mega-mcp/dist/index.js"],
      "env": {
        "MEGA_LINK": "https://mega.nz/folder/XXXX#YYYY"
      }
    }
  }
}

Without MEGA_LINK, pass a link argument to each tool call instead — handy for working with many links.

⚙️ Configuration

All environment variables are optional.

🎙️ Transcription (optional)

mega_transcribe is off by default. Turn it on by choosing a backend.

pip install faster-whisper

# macOS/Linux
export MEGA_TRANSCRIBE=command
export MEGA_TRANSCRIBE_CMD='python /path/to/mega-mcp/scripts/whisper_cli.py {input}'
export WHISPER_MODEL=base   # tiny | base | small | medium | large-v3

How it works: the media file is downloaded to a temp dir, ffmpeg (bundled) extracts mono 16 kHz audio and splits it into ~10-minute segments, each segment goes to your backend, and results are concatenated. Use the maxMinutes argument to transcribe just the start of a long file.

🛠️ Tool reference & example prompts

Once connected, you can drive everything in natural language:

🔬 How it works

            public MEGA link
                   │
        ┌──────────▼───────────┐
        │  megajs (no login)   │  load link → walk tree → resolve path → download
        └──────────┬───────────┘
                   │
   ┌───────────────┼───────────────────────────┐
   ▼               ▼                ▼            ▼
 browse /        read_file /      video_info   transcribe (opt-in)
 search          search_content   (ffprobe,    (download → ffmpeg
 (metadata)      (text + PDF via   header-only)  audio segments →
                  unpdf/pdf.js)                   backend → text)

• Public links are resolved with megajs — both /folder/…#key and /file/…#key forms.
• PDFs are extracted with unpdf (a serverless-friendly pdf.js build). Scanned/image-only PDFs yield no text (OCR not included).
• Video metadata is read by streaming only the header into ffprobe — a 1 GB file is probed in seconds without downloading it.
• Transcription downloads the file (needed because many mp4s store their index at the end and pipes aren't seekable), then segments audio so even hour-long lectures stay within backend size limits.

❓ FAQ

Do I need a MEGA account? No. mega-mcp only uses public share links.

Does it support private/account files? Not currently — public links only, by design. See the roadmap.

Are my files re-uploaded anywhere? No. Files are fetched directly from MEGA on demand. With the local transcription backend, nothing leaves your machine.

Can it read Word/Excel/PowerPoint? Not yet — text files and PDFs today. Office formats are on the roadmap.

Will big transcriptions time out in my client? The server emits MCP progress notifications, which keeps compatible clients alive. Use maxMinutes to cap long files.

Does ffmpeg need to be installed? No — ffmpeg-static and ffprobe-static bundle the binaries.

🗺️ Roadmap

• Optional account login for private files
• Office document extraction (docx, xlsx, pptx)
• OCR for scanned PDFs / images
• Cached content index for faster repeated searches
• Resource endpoints (expose files as MCP resources)

Have an idea? Open an issue 🙌

🤝 Contributing

Contributions are very welcome!

npm install
npm run build      # compile
npm run typecheck  # strict type check
npm run dev        # watch mode

1. Fork the repo and create a feature branch.
2. Keep the code style consistent and types strict.
3. Open a PR describing the change. Small, focused PRs are easiest to merge.

If you find a bug or want a feature, issues and ⭐ stars are both hugely appreciated.

📁 Project layout

src/
  index.ts       MCP server + tool definitions
  mega.ts        megajs wrapper (load link, walk tree, resolve path, download)
  pdf.ts         PDF text extraction (unpdf)
  video.ts       ffprobe streaming + metadata distillation
  transcribe.ts  optional, pluggable audio/video transcription
  types.d.ts     ambient module declaration for ffprobe-static
scripts/
  whisper_cli.py local faster-whisper transcription backend

📜 License

MIT © Ananya Fekeremariam (Anicodeth)