As developers, we're constantly seeking tools that streamline our workflows and unlock new possibilities. For anyone working with video content, especially in the age of short-form virality, the process of extracting compelling clips from longer videos can be a significant bottleneck. Enter the Lumiclip.ai API and its accompanying Model Context Protocol (MCP) server – a powerful solution designed to programmatically transform YouTube URLs into ready-to-post, AI-generated video clips.

The Lumiclip.ai API: A Developer's Gateway to AI Video Clipping

The Lumiclip.ai API exposes the core AI clipping engine behind the popular Lumiclip.ai consumer product. This means you can integrate advanced video analysis, intelligent clip selection, and automated editing directly into your applications, platforms, or custom workflows. Whether you're building a content management system, a social media scheduler, or an AI-powered video assistant, this API provides the programmatic control you need.

Core API Functionality:

The API is designed for simplicity and efficiency, focusing on a few key endpoints to manage the entire clip generation lifecycle.

POST /api/v1/clips/generate
Initiates AI-driven clip generation from a YouTube URL. Accepts optional start_time, end_time, and callback_url for webhook notifications. Returns a project_id for status tracking.

GET /api/v1/projects/{id}
Polls for the status of a specific project, including processing steps and a list of generated clips (sorted by AI score).

Base URL: https://api.lumiclip.ai
Authentication: Authorization: Bearer sk_live_... (API Key )

How it Works Under the Hood:

1.Submission: You send a POST request to /api/v1/clips/generate with a YouTube URL.

2.Processing: Lumiclip.ai downloads the video, transcribes it, and its AI engine analyzes the content to identify high-retention moments. These moments are then cut, reframed to a 9:16 aspect ratio with active-speaker tracking, and styled subtitles are burned in.

3.Asynchronous Results: The API responds immediately with a 202 Accepted status and a project_id. You can then poll the /api/v1/projects/{id} endpoint or configure a callback_url to receive a webhook when your clips are ready.

4.Retrieval: Once complete, you can fetch clip metadata and download URLs to integrate the generated clips into your application.

Seamless AI Agent Integration with the Lumiclip.ai MCP Server

For those leveraging AI agents and LLMs in their development stack, Lumiclip.ai takes integration a step further with its Model Context Protocol (MCP) server. The @lumiclip/mcp-server package provides a set of five typed tools that AI assistants can directly invoke as tool-use actions.

This means your AI agents (e.g., in Claude Desktop, Cursor, or other MCP-compatible environments) can programmatically request video clipping without needing to manage raw HTTP requests. The MCP server uses the same API key and credit pool, ensuring consistent access and usage.

MCP Tools for AI Agents:

•generate_clips: Initiate clip generation from a YouTube URL.

•get_project_status: Query the progress and retrieve clips for a given project.

•list_projects: Get an overview of recent projects.

•get_clip: Retrieve detailed information for a specific clip.

•check_usage: Monitor API usage and credit balance.

For remote MCP clients, a streamable HTTP endpoint is available at https://lumiclip.ai/api.

Get Started and Build Something Amazing!

Lumiclip.ai offers a compelling solution for developers looking to automate and enhance their video content workflows. With a free hour of processing credits available (no credit card required ), there's no better time to explore its capabilities.

Dive into the documentation and start building: lumiclip.ai/api