u/QencodeCorp

I’ve been experimenting with MCP and wanted to see how far I could push the “just ask Claude to do it” workflow.

In this case, I asked Claude to increase the resolution of a video. Behind the scenes, it uses an MCP server connected to Qencode to actually process the video.

Recorded a demo because I thought the interaction was pretty interesting.

I’m mostly curious about the UX here: does asking an LLM to handle video processing like this feel useful, or does it just add an unnecessary layer?

Would love feedback, especially from people upscaling video

Here’s a quick example using Qencode’s AI upscaling.

I started with a low-resolution video, selected the AI upscaling option, and ran the transcode.

The video shows the full setup in about 30 seconds, plus the before/after result.

Hope this is useful for anyone curious about how AI video upscaling works.

We’ve been experimenting with what video processing looks like when you can just ask for what you want.

We built an MCP for video transcoding, and for this demo I gave it an English video and asked it to add Spanish subtitles.

That’s it, no messing with subtitle files, timestamps, or transcoding settings manually.

The MCP handled the workflow and returned the processed video.

Still experimenting with what we can make it do. What video task should I try next?

Finding every moment where a logo appears, a product is mentioned, or a specific scene happens usually means manual review or a separate video AI workflow.

Video Intelligence runs inside the same Qencode job already processing the file. It can return timestamped search results, generate descriptions, categorize footage with your own labels, check content against custom moderation criteria, or answer questions about the video.

The result is a processed asset that is also searchable and easier to organize, moderate, and reuse.

How are you handling search and metadata across large video libraries today?

Tutorial here: https://hubs.la/Q04qtWJM0

We recently released an MCP server that lets AI assistants work directly with the Qencode API.

The goal was to make it easier for agents to submit transcoding jobs, check their status, and look up the right documentation without relying on a large custom prompt or manually composed API requests.

For common workflows, the transcode_video tool gives the model a more structured interface. For advanced use cases, start_encode2_raw provides full control over the Qencode request.

You can connect using your existing Qencode API key at:

https://mcp.qencode.com/mcp

Self-hosting is also supported.

We are still testing how different models choose between the structured and raw tools, so feedback is welcome, especially from anyone already building agent-based media workflows.

Technical write-up here