# Building a Remote MCP Server to Handle Large Files (Audio/Video)

> Source: <https://dev.to/warmcoding/building-a-remote-mcp-server-to-handle-large-files-audiovideo-3b6l>
> Published: 2026-11-01 08:12:16+00:00

The rise of AI agents (like Cursor and Claude Desktop) is incredible. But if you've ever tried to build a custom tool for them to process large files—like a 50MB audio track or a video—you already know the pain.

LLMs have strict token and payload limits. You can't just pass a local `.mp3` file into a prompt.

In this post, I'll share the architectural pattern I used to build a **Remote MCP (Model Context Protocol) Server** that allows an AI agent to process heavy media files entirely hands-free.

When you want an AI to process a file, the naive approach is to base64 encode it and send it. This blows up the context window instantly.

Alternatively, you can build a local MCP server that reads the file directly. But what if the processing requires heavy GPU compute that your local laptop doesn't have?

To solve this, I designed an MCP server that uses an **asynchronous pipeline** with presigned URLs. 

Here is the exact flow the AI agent follows:

`get_upload_url`` submit_task``check_task_status`` get_download_url`
This pattern completely decouples the AI's reasoning from the heavy lifting.

Here is what a prompt looks like in Cursor:

*"Extract the vocals from ./demo.mp3 and save the result locally."*

The AI handles the file system, calls the tools, waits for the GPU, and drops the finished file right back into your project folder. It feels like magic.

`stdio` isn't enough. Host your server and expose it via HTTP.
I built this entire architecture for **Sonicaura MCP** (an audio processing engine). It's completely open-source and published in the official MCP Registry if you want to dig into the code or try it out.

If you are building agents, I'd love to know: **How are you handling large file processing in your MCP servers?** Let me know in the comments!