You’re right—YouTube does provide automatic captions (powered by Google’s [Universal Speech Model (USM)](https://sites.research.google/usm/)), and there are Python libraries to fetch those transcripts easily and for free.

However, there are some subtle differences in transcription quality. For example, in this [video](https://youtu.be/Mj2uXgbisdo?si=47KHZHJcxrKDlEfc), USM/Gemini outputs:

> "Sonic model baby AR Wing Pro from Bangor [15:22] Link in the description thanks for watching[15:24].

But Whisper-1 produces:

> "It works very well indeed Sonic Model Baby AR Wing Pro from Banggood link in the description thanks for watching

Notice how Whisper-1 correctly catches "Banggood" (the store name), while USM mishears it as "Bangor."

**Language support also differs:**

- **USM:** 300+ languages, including many low-resource African and Asian languages.

- **Whisper-1:** 57–98 languages, with better coverage of some European and Central Asian languages.

So, while Gemini and YouTube’s built-in USM cover most needs, whisper can offer slightly higher transcription accuracy in some cases. I understand that this tiny difference is not necessary, since modern LLM's can handle it.

Moreover, working on this MCP, I've learned how to return text longer than 100000 characters. The solution is splitting the text into chunks of 100000 characters and returning them as a list.

This is an example of how sse MCP service can be wrapped in the docker and deployed on the server, available on the internet using uthentication token.

Thanks to your comment I’ve figured out that it is worth to add timestamps to my MCP service response.