YouTube MCP Server

Overview

The YouTube MCP Server bridges the gap between Claude (or any MCP‑compatible AI client) and the YouTube Data API v3. By exposing a set of high‑level tools—search, playlist creation, addition, listing and deletion—the server lets developers treat YouTube as a first‑class data source in their AI workflows. Instead of writing custom OAuth flows and API wrappers, an assistant can simply issue natural language commands such as “Search YouTube for piano tutorials” or “Create a playlist called My Favorites,” and the MCP server translates those into authenticated API calls, returning structured results that the assistant can present or act upon.

This capability is especially valuable for developers building content‑management, marketing, or research applications that rely on video data. For instance, a knowledge‑base assistant can surface the latest tutorial videos when answering user queries, or an automated content curator can batch‑add newly discovered clips to a curated playlist. By encapsulating the authentication logic and API rate limits behind an MCP interface, developers avoid repetitive boilerplate and can focus on higher‑level business logic.

Key features of the server include:

• Video search: Query YouTube’s vast catalog using keyword, channel, or filter parameters and receive concise metadata (title, URL, duration).
• Playlist management: Create new playlists, list all user playlists, and delete obsolete ones with a single command.
• Video addition: Add any video to an existing playlist by ID or URL, enabling automated content organization.
• OAuth 2.0 integration: The server handles the full OAuth flow, generating a refresh token that persists across sessions.
• Environment‑driven configuration: Credentials are supplied via environment variables, making deployment in CI/CD pipelines or local dev setups straightforward.

Typical use cases include:

• Content aggregation: A research assistant pulls the latest videos on a niche topic and compiles them into a shared playlist for stakeholders.
• Marketing automation: A social‑media bot searches for trending videos and adds them to a promotional playlist, then shares the link with audiences.
• Learning management: An educational platform automatically populates student‑specific playlists with instructional videos based on course progress.

Integration into AI workflows is seamless. Once the MCP server is registered in a client such as Claude Desktop, the assistant can invoke any of its tools through natural language prompts. The server returns structured JSON responses that the assistant can format into tables, embed links, or trigger further actions. Because the MCP abstracts away authentication and error handling, developers can rely on robust, repeatable interactions without maintaining custom API clients.

In summary, the YouTube MCP Server turns raw YouTube data into a ready‑to‑use resource for AI assistants, offering developers an efficient, secure, and scalable way to embed video search and playlist management into their applications.