json2video MCP Server

What does “json2video” MCP Server do?

The json2video MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the json2video API, enabling programmatic video creation through natural language or agent-driven workflows. By exposing tools for video generation and status checking, this MCP server allows developers, LLMs, and automation agents to create, customize, and monitor video projects using structured JSON. The server supports rich scene and element capabilities—including text, images, audio, components, and subtitles—making it ideal for dynamic video content creation. Designed for seamless integration with MCP-compatible platforms, json2video MCP Server enhances developer productivity by streamlining access to asynchronous video rendering and project management, all safeguarded by API key authentication and comprehensive error handling.

List of Prompts

No prompt templates are explicitly mentioned in the repository or documentation.

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List of Resources

No explicit MCP “Resources” are documented or described in the repository or README.

List of Tools

• generate_video
  Creates a video project using the json2video API. Allows detailed customization by specifying multiple scenes and elements (text, images, video, audio, HTML, subtitles, etc.). Returns a project ID for tracking.
• get_video_status
  Checks the render status of a previously submitted video project by project ID, enabling asynchronous workflows and progress monitoring.

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Use Cases of this MCP Server

• Automated Video Content Generation
  Developers and agents can generate marketing, educational, or social media videos programmatically, reducing manual editing and enabling rapid content iteration.
• Dynamic Scene Composition
  LLM-driven workflows can assemble complex videos by dynamically specifying scenes and media elements, suitable for personalized or data-driven video outputs.
• Status Monitoring for Long Renders
  Asynchronous video rendering allows agents to check and report on the status of video creation, improving user experience in applications requiring progress feedback.
• Integration with AI Content Pipelines
  Easily fits into larger, multi-step AI workflows where video output is a step—such as summarizing content, generating visuals, and compiling final videos automatically.
• Component-Based Video Assembly
  Enables composable video generation by merging text, graphics, audio, and subtitles, useful for accessibility and localization workflows.

How to set it up

Windsurf

No setup instructions for Windsurf are mentioned in the repository or README.

Claude

No setup instructions for Claude are mentioned in the repository or README.

Cursor

1. Open Cursor Settings.
2. Go to Features > MCP Servers.
3. Click “+ Add New MCP Server”.
4. Enter:
   • Name: “json2video-mcp” (or your preferred name)
   • Type: “command”
   • Command:

     env JSON2VIDEO_API_KEY=your_api_key_here npx -y @omerrgocmen/json2video-mcp
     

5. Alternatively, add to your global MCP server configuration:

   {
     "mcpServers": {
       "json2video-mcp": {
         "command": "npx",
         "args": ["-y", "@omerrgocmen/json2video-mcp"],
         "env": {
           "JSON2VIDEO_API_KEY": "your_api_key_here"
         }
       }
     }
   }
   

6. Replace your_api_key_here with your actual json2video API key (obtainable from json2video.com ).
7. Refresh the MCP server list after saving.

Cline

No setup instructions for Cline are mentioned in the repository or README.

Securing API Keys

• API keys must be provided via environment variable JSON2VIDEO_API_KEY.

• Example (in the configuration JSON):

  {
    "mcpServers": {
      "json2video-mcp": {
        "command": "npx",
        "args": ["-y", "@omerrgocmen/json2video-mcp"],
        "env": {
          "JSON2VIDEO_API_KEY": "your_api_key_here"
        }
      }
    }
  }
  

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "json2video-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “json2video-mcp” to whatever the actual name of your MCP server is, and replace the URL with your own MCP server URL.

Overview

Our opinion

json2video MCP is a focused, well-documented server for exposing video generation as a tool to LLMs and agents. It lacks some advanced MCP features (like roots, resources, sampling, or prompt templating), but is straightforward to install and use for its intended purpose. If you only need video generation tools, this MCP is functional and easy to integrate, but may not be as extensible as others.

MCP Score

Based on the above, I would rate this MCP server a 5/10: It is functionally sound for its core purpose, but lacks broader MCP ecosystem features and extensibility.