MCP Video Server

Overview of the MCP Video App

The MCP Video App solves a common bottleneck in AI‑assisted media workflows: the lack of a lightweight, server‑side solution that can ingest user videos, process them with standard tools such as FFmpeg, and expose the results through a Model Context Protocol (MCP) interface. By providing a pre‑built Node.js server that handles uploads, transcoding, and file management, developers can focus on building higher‑level AI applications—such as summarization, captioning, or content moderation—without reinventing the file handling plumbing.

At its core, the server offers a RESTful API for uploading videos and retrieving processed outputs. The upload endpoint accepts multipart form data, stores the raw file in an directory, and triggers FFmpeg to convert or trim the media as needed. Processed files are saved in a separate folder, making it straightforward to reference them later. The server also serves static files from a directory, enabling quick prototyping of web UIs that can display progress bars or preview thumbnails.

Key capabilities include:

• Seamless integration with FFmpeg: The server automatically detects the installed FFmpeg binary and uses it for any required video transformations, ensuring consistent results across platforms.
• Scalable file handling: By separating uploads and processed files, developers can implement cleanup policies or archival strategies without affecting live processing.
• Cross‑origin support: The inclusion of CORS middleware allows the server to be consumed from web clients hosted on different domains, a common scenario in distributed AI pipelines.
• Extensibility: The modular Express architecture makes it trivial to add new endpoints—such as a route that extracts frame data or audio transcripts—thereby expanding the MCP’s toolset.

Typical use cases span from AI research labs that need a quick way to batch‑process training videos, to production systems where an assistant must convert user‑submitted clips into standardized formats before feeding them into downstream models. For example, a chatbot that offers video editing services can delegate the heavy lifting to this MCP server, exposing only a simple “edit” tool in its prompt language while the underlying video manipulation is handled efficiently on the server side.

Because the MCP Video App wraps a well‑known open‑source toolchain (Node.js, Express, FFmpeg) in an MCP‑ready API, it delivers a unique advantage: developers can deploy the server with minimal configuration yet gain full control over file handling, security (via express middleware), and integration points. This makes it an ideal foundation for building sophisticated AI assistants that need to interact with real‑world media without the overhead of managing low‑level processing pipelines.