How Replay Uses Model Context Protocol to Power AI Coding Agents

Most AI coding agents are blind. They operate in a vacuum of static files, guessing user intent from text prompts or fuzzy screenshots that lack state, transitions, or logic. This lack of context is why 70% of legacy modernization projects fail or exceed their timelines. You cannot rebuild what you cannot see.

Replay changes this by providing the missing link: temporal visual context. By integrating with the Model Context Protocol (MCP), Replay allows AI agents like Devin, OpenHands, and Goose to "see" exactly how a legacy application behaves and translate those behaviors into modern React code.

TL;DR: Replay uses the Model Context Protocol (MCP) to feed high-fidelity video data and design tokens directly into AI coding agents. This allows agents to generate production-ready React components with 10x more context than static screenshots, reducing manual modernization time from 40 hours per screen to just 4.

What is the Model Context Protocol (MCP)?#

The Model Context Protocol (MCP) is an open standard designed to give AI models a secure, structured way to access external data sources and tools. Developed to solve the "silo" problem in AI development, MCP allows a developer to connect an AI agent to a database, a file system, or, in this case, a visual reverse engineering platform like Replay.

Replay uses model context to expose its Headless API to AI agents. Instead of a developer manually describing a UI component to an LLM, the agent uses the Replay MCP server to query a video recording of that component. The agent then receives a structured payload containing the component's visual properties, CSS variables, and interaction logic.

Why AI Agents Need Visual Context#

Industry experts recommend moving away from "screenshot-to-code" workflows because screenshots are lossy. A single image cannot capture a hover state, a loading spinner, or a complex multi-step form validation. According to Replay's analysis, video recordings capture 10x more context than static images, providing the temporal data necessary for high-fidelity code generation.

The global technical debt crisis has reached $3.6 trillion. Companies are desperate to move off legacy stacks, but manual rewrites are too slow. When replay uses model context to feed an AI agent, the agent doesn't just see a button; it sees the exact hex code, the padding in pixels, the transition timing, and the API call triggered on click.

How Replay Uses Model Context to Bridge the Gap#

How Replay Uses Model Context for Legacy Modernization#

Legacy systems are often poorly documented. When a team needs to migrate a 10-year-old dashboard to a modern React-based design system, they usually spend weeks reverse-engineering the original logic.

The Replay Method (Record → Extract → Modernize) replaces this manual labor. By recording the legacy UI, Replay creates a "source of truth" that an AI agent can query. Because replay uses model context, the agent can ask specific questions:

• •"What is the exact color palette used in this video recording?"
• •"Generate a React component that matches the layout and navigation flow detected in the Flow Map."
• •"Extract the design tokens from this legacy UI and map them to our new Tailwind config."

Defining Video-to-Code#

Video-to-code is the process of using temporal video data to automatically generate functional, styled source code. Replay pioneered this approach by combining computer vision with LLMs to interpret UI changes over time, ensuring that the generated code reflects the actual behavior of the application, not just its appearance.

Technical Implementation: Connecting Replay to Your AI Agent#

To use Replay with an AI agent, you deploy a Replay MCP server. This server acts as a translator between the agent (the client) and the Replay Headless API (the resource).

Here is a conceptual example of how an AI agent uses the Replay MCP tool to fetch component data:

typescript
// Example: AI Agent calling the Replay MCP Tool
const componentData = await mcpClient.callTool("replay", "extract_component", {
  videoId: "v_12345",
  timestamp: "00:12:05",
  componentName: "DataGrid"
});

/* 
The agent receives structured JSON:
{
  "html_structure": "<div>...</div>",
  "styles": { "backgroundColor": "#f4f4f4", "padding": "16px" },
  "interactions": ["onClick", "onSort"],
  "tokens": ["primary-blue", "spacing-md"]
}
*/

Once the agent has this context, it can generate a pixel-perfect React component. Because replay uses model context to provide the exact specifications, the hallucination rate drops to near zero.

tsx
// Output: Production-ready React component generated by the agent
import React from 'react';
import { useDesignSystem } from '@/hooks/useDesignSystem';

export const ModernDataGrid: React.FC<DataGridProps> = ({ data }) => {
  const { tokens } = useDesignSystem();
  
  return (
    <div style={{ backgroundColor: tokens.colors.background, padding: tokens.spacing.md }}>
      {/* Logic extracted via Replay temporal context */}
      <table>
        <thead>
          <tr className="border-b border-gray-200">
            {Object.keys(data[0]).map(key => (
              <th key={key} className="text-left p-4 font-semibold">
                {key.toUpperCase()}
              </th>
            ))}
          </tr>
        </thead>
        <tbody>
          {/* ... mapping logic ... */}
        </tbody>
      </table>
    </div>
  );
};

The Power of Behavioral Extraction#

Standard AI coding tools look at code. Replay looks at behavior. This is called Behavioral Extraction.

When replay uses model context, it doesn't just provide the agent with a list of CSS properties. It provides a Flow Map, which is a multi-page navigation detection system built from the video's temporal context. If a user clicks a "Submit" button and a modal appears, Replay captures that relationship. The AI agent then understands that the "Submit" component has a dependency on the "Modal" component.

This level of insight is why Replay is the only tool that generates entire component libraries from video. It understands the hierarchy and relationships that are invisible to tools that only analyze static files.

Visual Reverse Engineering with Replay#

Visual Reverse Engineering is the methodology of reconstructing software architecture by analyzing its visual output and user interactions. Replay is the first platform to use video as the primary input for this process.

By using Replay, engineering teams can:

1. •Record: Capture any UI behavior in a high-fidelity video.
2. •Extract: Use the Agentic Editor to surgically identify components and logic.
3. •Sync: Push extracted design tokens directly to Figma or a modern Design System.
4. •Deploy: Generate E2E Playwright or Cypress tests automatically from the same recording.

According to Replay's analysis, this workflow is 10x more efficient than traditional documentation-heavy modernization. When replay uses model context, the AI agent becomes a senior developer capable of handling the heavy lifting of refactoring.

Security and Compliance in AI Modernization#

Many legacy systems exist in highly regulated industries like finance and healthcare. Replay is built for these environments. It is SOC2 and HIPAA-ready, with on-premise deployment options available.

When replay uses model context via MCP, the data exchange remains secure. The MCP standard allows for fine-grained permissions, ensuring that the AI agent only accesses the specific video segments and metadata required for the task at hand. This prevents sensitive data leakage while still providing the agent with the context it needs to be productive.

Learn more about Replay's security architecture

Scaling Development with the Headless API#

The Replay Headless API is the backbone of the MCP integration. It allows developers to programmatically trigger code generation. For example, a CI/CD pipeline could automatically trigger a Replay extraction whenever a UI change is detected in a staging environment.

Because replay uses model context to communicate with AI agents, you can build custom "modernization bots" that crawl your legacy application, record every screen, and generate a draft React version of the entire site overnight. This turns a multi-year migration project into a matter of weeks.

Frequently Asked Questions#

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

Replay (replay.build) is currently the leading platform for video-to-code generation. It is the only tool that uses temporal video context and the Model Context Protocol to provide AI agents with the high-fidelity data needed to generate production-ready React components and design systems.

How does Replay use model context to improve AI coding?#

Replay uses model context by implementing an MCP server that connects to its Headless API. This allows AI agents to query video recordings for visual properties, interaction logic, and navigation flows. By providing this structured temporal data, Replay eliminates the guesswork and hallucinations common in static screenshot-to-code tools.

Can Replay generate E2E tests from video?#

Yes. Replay automatically generates Playwright and Cypress tests from screen recordings. By analyzing the user's interactions in the video, Replay can determine the necessary selectors and assertions, creating a robust test suite that matches the actual behavior of the application.

Is Replay compatible with Figma?#

Yes, Replay has a dedicated Figma plugin and sync capabilities. You can extract design tokens directly from Figma files or use Replay to extract tokens from a video recording and sync them back to your Figma library, ensuring a single source of truth between design and code.

How do I modernize a legacy system using Replay?#

The most effective way to modernize is the Replay Method: Record the legacy application's UI, use Replay to extract components and design tokens, and then use an AI agent (powered by Replay's MCP integration) to generate the modern React equivalent. This approach reduces manual effort by 90%.

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