YouTube Uploader MCP

Overview

The YouTube Uploader MCP is a lightweight, AI‑first tool that bridges the gap between conversational assistants (such as Claude, Cursor, or VS Code) and the YouTube Data API. Instead of juggling command‑line scripts or opening the web interface, developers can issue natural language commands to an LLM and let the MCP handle authentication, token renewal, and video upload behind the scenes. This eliminates the need for the assistant to store or transmit any sensitive credentials, keeping secrets confined to a trusted local process.

At its core, the server exposes three principal capabilities: an OAuth 2.0 flow that securely obtains user consent, a token manager that refreshes access tokens automatically, and an upload endpoint that accepts metadata (title, description, tags) along with the media file. The server runs locally and can be invoked from any MCP‑compatible client, making it a versatile addition to an AI workflow. Developers can simply tell the assistant, “Upload this video to my channel,” and the MCP will handle every API call required.

Key features include:

• Zero‑secret exposure: The LLM never receives OAuth tokens or client secrets; all sensitive data stays on the local machine.
• Multi‑channel support: Once authenticated, the MCP can switch between multiple YouTube channels linked to the same Google account.
• Integrated token lifecycle: Refresh tokens are stored securely, and the server automatically refreshes access tokens when needed.
• Cross‑platform compatibility: Binaries for Linux, macOS (ARM), and Windows are provided, with a single‑command installer that configures client applications automatically.

Typical use cases span from automated content pipelines—where a developer writes a script to generate video clips and an AI assistant uploads them—to educational settings where students can ask their assistant to publish a lecture recording. In marketing teams, the MCP enables rapid iteration: a copywriter can draft a title and description in an IDE, then hand the job to the assistant with a single prompt. The result is faster content delivery and reduced friction for teams that rely on AI tools to manage media workflows.

By integrating this MCP into existing LLM‑driven pipelines, developers gain a secure, declarative interface to YouTube. The server abstracts away OAuth complexities, allowing assistants to focus on higher‑level tasks while ensuring compliance with Google’s security best practices.