Cloudglue MCP Server Overview

The Cloudglue MCP Server bridges the gap between raw video content and large‑language models by converting unstructured video data into structured, queryable metadata. By exposing a Model Context Protocol (MCP) interface, it lets AI assistants—such as Claude Desktop or Cursor—access Cloudglue’s video analysis capabilities directly from their workflow. This eliminates the need for manual extraction or custom API integrations, allowing developers to focus on higher‑level application logic rather than low‑level video processing.

At its core, the server authenticates with a Cloudglue API key and offers a suite of tools that transform video URLs into actionable information. These include , which generates natural‑language summaries; , which identifies people, objects, and locations; and , which delineates logical segments within a clip. For videos already hosted on Cloudglue, the server accepts a reference; for external sources it supports YouTube links, public MP4 URLs, and data‑connector endpoints. This flexibility means developers can seamlessly integrate locally stored footage or publicly shared content without altering their existing MCP client configuration.

The value proposition for developers lies in the server’s zero‑code integration path. A simple JSON entry in an MCP client points to the Cloudglue binary, and the assistant automatically gains access to all video‑analysis tools. This is especially useful in scenarios such as content moderation, automated subtitle generation, or building knowledge bases from training videos. By turning video streams into structured prompts, the server enables more precise LLM queries and richer conversational contexts.

Key features include:

• Unified URL handling for Cloudglue, YouTube, and public HTTP videos.
• Comprehensive toolset covering description, entity extraction, and chapter segmentation.
• Extensible MCP interface, allowing future addition of new tools or data connectors without breaking client compatibility.
• Cross‑platform support, with a ready‑made Desktop Extension for Claude and command‑line invocation for other MCP clients.

In practice, a content team could upload a training video to Cloudglue, run the MCP server locally, and then ask an AI assistant “What are the main topics covered in this video?” The assistant would retrieve the metadata via , summarize each section, and provide a concise answer—all without any manual preprocessing. Similarly, a compliance officer could use to audit footage for sensitive content, automatically flagging segments that contain restricted terms.

Overall, the Cloudglue MCP Server empowers AI developers to treat video as first‑class data. By abstracting the complexities of video analysis behind a standard protocol, it unlocks new possibilities for AI‑driven media workflows, knowledge extraction, and automated content curation.