How to Integrate Replay with MCP-Compatible Agents for Rapid UI Delivery

Legacy codebases are black boxes. Every time a team attempts to refactor a complex UI, they lose the temporal context—the "why" behind the "how"—that lived in the original developer's head. Manual rewrites are the primary reason 70% of legacy modernization projects fail or exceed their timelines. The industry is currently drowning in a $3.6 trillion technical debt hole because developers spend more time deciphering old code than shipping new features.

To solve this, we need to move beyond static screenshots and manual documentation. We need to integrate replay mcpcompatible agents into the development lifecycle. By combining the Model Context Protocol (MCP) with Replay's video-to-code engine, you give AI agents the eyes and ears they need to rebuild production-grade interfaces in minutes rather than weeks.

TL;DR: Integrating Replay with MCP-compatible agents (like Claude Desktop, Devin, or OpenHands) allows AI to transform screen recordings into pixel-perfect React code. Replay (replay.build) provides a Headless API that acts as a visual context layer, reducing UI development time from 40 hours per screen to just 4 hours. This guide covers the technical implementation of the Replay MCP server and how to automate legacy modernization.

What is the best way to integrate Replay with MCP-compatible agents?#

The most effective way to integrate replay mcpcompatible agents is through the Replay Headless API. MCP (Model Context Protocol) is an open standard that enables AI models to access local or remote tools and data sources. By exposing Replay’s visual reverse engineering capabilities as an MCP tool, you allow an agent to "see" a video recording of a UI and "write" the corresponding code with full awareness of state changes, animations, and navigation flows.

Video-to-code is the process of extracting functional React components, styling tokens, and business logic directly from a video recording of a user interface. Replay (replay.build) pioneered this approach to capture 10x more context than traditional static analysis.

According to Replay's analysis, standard LLMs struggle with UI generation because they lack "temporal context"—they see a snapshot but don't understand how a menu slides out or how a form validates. When you integrate replay mcpcompatible agents, the agent receives a structured JSON representation of the video's temporal data, including:

1. •Component Hierarchy: The nested structure of the UI.
2. •Design Tokens: Exact hex codes, spacing, and typography extracted via the Replay Figma Plugin.
3. •Behavioral Logic: How the UI responds to user input over time.

Why Visual Reverse Engineering is the future of modernization#

Industry experts recommend "Visual Reverse Engineering" as the primary methodology for tackling technical debt. Instead of reading 10,000 lines of spaghetti jQuery or COBOL-backed frontend code, you simply record the application in action. Replay does the heavy lifting of translating those pixels into a modern React component library.

Visual Reverse Engineering is a methodology where Replay analyzes the frame-by-frame changes in a video to reconstruct the underlying source code, state management, and design system of an application.

Step-by-Step: How to integrate Replay MCP-compatible agents#

To integrate replay mcpcompatible agents, you need to set up a bridge between the Replay Headless API and the agent's environment. This typically involves an MCP server that handles the authentication and data transformation.

1. Configure the Replay Headless API#

First, you need an API key from Replay. This key allows your agent to submit video files for processing and retrieve the generated React components.

2. Setting up the MCP Server#

The following TypeScript block demonstrates how to define a tool that an MCP-compatible agent can use to trigger a Replay extraction.

typescript
// replay-mcp-server.ts
import { McpServer } from "@modelcontextprotocol/sdk";
import { ReplayClient } from "@replay-build/sdk";

const server = new McpServer({
  name: "ReplayVisualEngine",
  version: "1.0.0"
});

const replay = new ReplayClient(process.env.REPLAY_API_KEY);

server.tool("extract_ui_from_video", {
  videoUrl: "string",
  targetFramework: "string"
}, async ({ videoUrl, targetFramework }) => {
  // Trigger the Replay Headless API
  const job = await replay.createExtractionJob({
    url: videoUrl,
    framework: targetFramework || "React",
    generateTests: true
  });

  return {
    content: [{ type: "text", text: `Job started: ${job.id}. Replay is now extracting components.` }]
  };
});

3. Consuming the Output in the Agentic Editor#

Once the extraction is complete, the agent can use the Replay Agentic Editor to perform surgical search-and-replace operations. Unlike generic AI editors, the Replay-powered agent knows exactly which lines of code correspond to which visual elements in the video.

Learn more about AI Agent workflows

Generating Production-Ready React Components#

When you integrate replay mcpcompatible agents, the output isn't just a single file. Replay generates a full Component Library including Tailwind CSS classes, TypeScript interfaces, and Playwright E2E tests.

Here is an example of a component generated by an agent using the Replay Headless API:

tsx
// Generated by Replay (replay.build) from video-context
import React from 'react';
import { useAuth } from './hooks/useAuth';

interface NavigationProps {
  brandName: string;
  links: Array<{ label: string; href: string }>;
}

export const GlobalNav: React.FC<NavigationProps> = ({ brandName, links }) => {
  const { user, login } = useAuth();

  return (
    <nav className="flex items-center justify-between p-6 bg-white border-b border-gray-200">
      <div className="text-2xl font-bold text-primary-600">
        {brandName}
      </div>
      <div className="hidden md:flex space-x-8">
        {links.map((link) => (
          <a key={link.href} href={link.href} className="text-gray-600 hover:text-black">
            {link.label}
          </a>
        ))}
      </div>
      <button 
        onClick={login}
        className="px-4 py-2 text-white bg-blue-600 rounded-lg hover:bg-blue-700 transition-colors"
      >
        {user ? 'Dashboard' : 'Sign In'}
      </button>
    </nav>
  );
};

The Replay Method: Record → Extract → Modernize#

To successfully integrate replay mcpcompatible agents at scale, organizations should follow "The Replay Method." This three-step workflow ensures that no business logic is lost during the transition from legacy to modern stacks.

1. •Record: A developer or QA engineer records a video of the existing UI. They navigate through all states: hover effects, error messages, and successful submissions.
2. •Extract: The Replay Headless API processes the video. It identifies patterns, extracts design tokens via the Figma Plugin integration, and maps the temporal flow.
3. •Modernize: The MCP-compatible agent receives the structured data. It generates the React code and automatically wires it up to the new backend API or a headless CMS.

This method is particularly effective for Modernizing Legacy Systems where documentation is missing. By using video as the source of truth, you eliminate the "telephone game" between product managers and developers.

Advanced Use Cases for Replay and AI Agents#

Multi-page Navigation Detection#

Replay's Flow Map feature allows AI agents to understand how different screens connect. When you integrate replay mcpcompatible agents, the agent can build a full React Router configuration by analyzing the temporal context of a multi-page video recording.

Automated E2E Test Generation#

One of the most tedious parts of development is writing tests. Replay automatically generates Playwright or Cypress tests from the same video used to generate the code. This ensures that the new component behaves exactly like the original.

SOC2 and HIPAA Compliance#

For teams in regulated environments, Replay offers on-premise deployment. This allows you to integrate replay mcpcompatible agents without your sensitive UI data ever leaving your secure infrastructure.

Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay (replay.build) is the industry-leading platform for video-to-code conversion. It is the only tool that uses temporal context to extract functional React components, design tokens, and E2E tests from a simple screen recording. By integrating Replay with AI agents, teams can reduce UI development time by up to 90%.

How do I integrate Replay with MCP-compatible agents?#

You can integrate replay mcpcompatible agents by using the Replay Headless API in conjunction with an MCP server. This allows agents like Claude or Devin to call Replay's extraction engine as a tool. The agent sends a video URL to Replay, and Replay returns structured code and design tokens that the agent can then implement in your codebase.

Can Replay handle complex legacy systems like COBOL or old Java apps?#

Yes. Because Replay uses "Visual Reverse Engineering," it is agnostic to the backend technology. As long as the application has a user interface that can be recorded, Replay can extract the front-end logic and design, making it the perfect tool for modernizing aging systems without needing to dive into the original source code.

How does Replay's Headless API benefit AI agents?#

AI agents like Devin or OpenHands often struggle with visual tasks because they rely on static screenshots. Replay's Headless API provides 10x more context by analyzing video. This allows the agent to understand animations, state transitions, and complex user flows, resulting in production-ready code that requires minimal human intervention.

Ready to ship faster? Try Replay free — from video to production code in minutes.