YouTube MCP Server

The YouTube MCP Server fills a critical gap for developers building AI‑powered applications that need instant, structured access to YouTube data. Instead of writing custom API wrappers or dealing with OAuth flows, the server exposes a clean MCP interface that lets Claude and other AI assistants query videos, channels, comments, transcripts, and even perform comparative analytics—all through a simple HTTP endpoint. This abstraction dramatically reduces the friction of integrating YouTube content into conversational agents, data pipelines, or analytics dashboards.

At its core, the server offers a set of resources and tools that mirror YouTube’s own data model. Resources such as and return rich JSON payloads with metadata, view counts, engagement metrics, and more. Tools like provide advanced filtering (by region, category, or upload date) and pagination support, while and let developers harvest community sentiment or generate clean, searchable text from captions. These capabilities are exposed over a Streamable HTTP transport that is fully compatible with Smithery’s hosting platform, ensuring low latency and robust session handling.

Developers can leverage this server in a variety of real‑world scenarios. For instance, an educational AI tutor could fetch the latest videos on a topic and summarize their key points for students. A marketing analytics tool might compare view‑through rates across a brand’s video catalog, automatically flagging underperforming content. Content creators can use the transcript extraction features to generate closed captions in multiple languages, improving accessibility and SEO. Because all operations are stateless and HTTP‑based, integrating the server into existing CI/CD pipelines or microservice architectures is straightforward.

What sets the YouTube MCP Server apart is its combination of breadth and simplicity. It supports both high‑level searches and low‑level transcript manipulation, all while maintaining a single, well‑documented endpoint (). The server’s migration to Streamable HTTP transport means it can scale horizontally behind a load balancer, handle concurrent sessions gracefully, and provide graceful error handling. Moreover, the server’s design aligns with MCP best practices—resources are URL‑like, tools have clear names and parameters, and responses are deterministic JSON—making it a drop‑in component for any AI workflow that needs reliable YouTube data.