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Jsoncut MCP Server

Organization
jsoncut

Official MCP server for jsoncut.com , an API that generates images and videos from JSON scripts. It supports creating and validating JSON files, while exposing schema and syntax as resources for LLMs to streamline media generation workflows.

Publisherjsoncut
Repositoryjsoncut-mcp-server
LanguageJavaScript
Forks
2
Stars
1
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    Jsoncut MCP Server exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    1 stars and 2 forks from the linked repository.

[!WARNING] ⚠️ OUTDATED REPOSITORY - This repository is no longer maintained

A public MCP server is now available and this package is deprecated.
Please use the official public server instead: https://mcp.jsoncut.com/mcp

For more information, see: https://docs.jsoncut.com/docs/mcp/overview



🚀 Features

  • 🎨 Image Generation: Create JSON configurations for image composition with layers, positioning, and effects
  • 🎬 Video Generation: Create JSON configurations for video rendering with clips, transitions, and audio
  • Configuration Validation: Validate configs against the Jsoncut API before submission
  • 📋 Schema Resources: JSON schemas automatically available as MCP resources
  • 🔑 Flexible Authentication: API key via environment variable or .env file

📦 Quick Start

Using npx (Recommended for Local)

bash
export JSONCUT_API_KEY=your_api_key_here
npx -y @jsoncut/mcp-server

Using Remote Server (Recommended)

A public MCP server is available at https://mcp.jsoncut.com. No installation needed - just configure your MCP client with your API key:

json
{
  "jsoncut": {
    "url": "https://mcp.jsoncut.com/mcp",
    "headers": {
      "x-api-key": "your_jsoncut_api_key_here"
    }
  }
}

Using Docker Locally (Optional)

You can also run your own local server using Docker:

bash
# Pull and run from Docker Hub
docker run -d \
  --name jsoncut-mcp \
  -p 3210:3000 \
  centerbit/jsoncut-mcp-server:latest

# Access at: http://localhost:3210/mcp

Or use Docker Compose:

bash
# Start the service
docker-compose up -d

# Access at: http://localhost:3210/mcp

📖 See DOCKER.md for complete Docker deployment guide

Get Your API Key

Get your Jsoncut API key at jsoncut.com

bash
# Set as environment variable
export JSONCUT_API_KEY=your_api_key_here

# Or create .env file
cp .env.example .env
# Edit .env and add: JSONCUT_API_KEY=your_api_key_here

🎯 MCP Client Configuration

Remote Server (Recommended)

Use the public server at https://mcp.jsoncut.com:

Cursor IDE

Open Cursor SettingsFeaturesMCP Servers"+ Add New MCP Server"

json
{
  "jsoncut": {
    "url": "https://mcp.jsoncut.com/mcp",
    "headers": {
      "X-API-Key": "your_jsoncut_api_key_here"
    }
  }
}

Claude Desktop

Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

json
{
  "mcpServers": {
    "jsoncut": {
      "url": "https://mcp.jsoncut.com/mcp",
      "headers": {
        "X-API-Key": "your_jsoncut_api_key_here"
      }
    }
  }
}

Local npx Mode

For local development without network access:

Cursor IDE

json
{
  "jsoncut": {
    "command": "npx",
    "args": ["-y", "@jsoncut/mcp-server"],
    "env": {
      "JSONCUT_API_KEY": "your_api_key_here"
    }
  }
}

Claude Desktop

json
{
  "mcpServers": {
    "jsoncut": {
      "command": "npx",
      "args": ["-y", "@jsoncut/mcp-server"],
      "env": {
        "JSONCUT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Local Docker Server

If you're running your own local Docker server:

Cursor IDE

json
{
  "jsoncut": {
    "url": "http://localhost:3210/mcp",
    "headers": {
      "X-API-Key": "your_jsoncut_api_key_here"
    }
  }
}

Claude Desktop

json
{
  "mcpServers": {
    "jsoncut": {
      "url": "http://localhost:3210/mcp",
      "headers": {
        "X-API-Key": "your_jsoncut_api_key_here"
      }
    }
  }
}

📚 MCP Resources

The server automatically exposes JSON schemas as MCP resources:

  • schema://image - Complete image generation schema
  • schema://video - Complete video generation schema

AI agents can read these directly without tool calls for fast access to all configuration options.


🛠️ Available Tools

create_image_config

Create JSON configurations for image generation with a layer-based system.

Layer Types:

  • image: Display images with fit modes (cover, contain, fill, inside, outside)
  • text: Text with custom fonts, alignment, wrapping, and effects
  • rectangle: Rectangular shapes with fill, stroke, and rounded corners
  • circle: Circular and elliptical shapes
  • gradient: Linear or radial color gradients

Positioning:

  • Pixel coordinates: { x: 100, y: 50 }
  • Position strings: center, top, bottom, top-left, top-right, etc.
  • Position objects: { x: 0.5, y: 0.5, originX: "center", originY: "center" }

Example:

json
{
  "width": 1200,
  "height": 630,
  "layers": [
    {
      "type": "gradient",
      "x": 0, "y": 0, "width": 1200, "height": 630,
      "gradient": {
        "type": "linear",
        "colors": ["#667eea", "#764ba2"],
        "direction": "diagonal"
      }
    },
    {
      "type": "text",
      "text": "Welcome to Jsoncut",
      "position": "center",
      "fontSize": 64,
      "color": "#ffffff"
    }
  ]
}

create_video_config

Create JSON configurations for video generation with clips, layers, and transitions.

Key Features:

  • Clips: Sequential video segments with individual layers
  • Layer Types: video, image, title, subtitle, news-title, audio, gradients, and more
  • Transitions: 75+ effects (fade, wipe, circle, cube, glitch, zoom, etc.)
  • Audio: Background music, multiple tracks, normalization, and ducking

Example:

json
{
  "width": 1920,
  "height": 1080,
  "fps": 30,
  "defaults": {
    "duration": 3,
    "transition": { "name": "fade", "duration": 1 }
  },
  "clips": [
    {
      "layers": [
        { "type": "title", "text": "Welcome", "position": "center" }
      ]
    }
  ]
}

validate_config

Validate configurations against the Jsoncut API before submission.

Parameters:

  • type: "image" or "video"
  • config: Configuration object to validate
  • apiKey: Optional API key (uses environment if not provided)

Returns:

  • Validation status
  • Estimated token cost
  • Error details (if any)
  • Detected resources with sizes

get_image_schema / get_video_schema

Get complete JSON schemas for image or video generation.

Note: Schemas are also available as MCP resources (schema://image and schema://video) which AI agents can access directly without tool calls.


📖 Workflow

  1. Create Configuration: Use create_image_config or create_video_config
  2. Validate (optional): Call validate_config if you have actual file paths
  3. Submit: Use the configuration with the Jsoncut API

The schemas are automatically available as MCP resources, so AI agents have instant access to all configuration options.


📁 File Paths

Use placeholder paths in configurations:

/image/2024-01-15/userXXX/photo.jpg
/video/2024-01-15/userXXX/video.mp4
/audio/2024-01-15/userXXX/music.mp3
/font/2024-01-15/userXXX/CustomFont.ttf

Supported formats:

  • Images: png, jpg, jpeg, gif, webp
  • Videos: mp4, mov, avi, webm
  • Audio: mp3, wav, m4a, aac
  • Fonts: ttf, otf, woff, woff2

🧪 Testing

Use the MCP Inspector for interactive testing:

bash
export JSONCUT_API_KEY=your_api_key_here
npm run inspector

🔧 Development

Local Development

bash
# Clone and install
git clone https://github.com/jsoncut/jsoncut-mcp-server.git
cd jsoncut-mcp-server
npm install

# Build
npm run build

# Watch mode
npm run watch

# Run locally
node dist/index.js

Configuration with Local Build

For Cursor/Claude Desktop, use the local build:

json
{
  "jsoncut": {
    "command": "node",
    "args": ["/absolute/path/to/jsoncut-mcp-server/dist/index.js"],
    "env": {
      "JSONCUT_API_KEY": "your_api_key_here"
    }
  }
}

📝 Examples

See the examples/ directory for complete configurations:

  • image-example.json - Image generation with multiple layer types
  • video-example.json - Video generation with clips and transitions

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.


📄 License

MIT License - see LICENSE file for details.


🔗 Links


Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "jsoncut": {
      "command": "npx",
      "args": [
        "-y",
        "@jsoncut/mcp-server"
      ],
      "env": {
        "JSONCUT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Use Jsoncut MCP Server MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Jsoncut MCP Server is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Jsoncut MCP Server as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Jsoncut MCP Server MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "jsoncut-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@jsoncut/mcp-server"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Jsoncut MCP Server MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Jsoncut MCP Server MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Jsoncut MCP Server to help me with this task?
Jsoncut MCP Server
Sure. I read it.
Here is what I found using Jsoncut MCP Server.

Frequently asked questions

What is the Jsoncut MCP Server MCP server used for?

Jsoncut MCP Server is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Jsoncut MCP Server MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Jsoncut MCP Server with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Jsoncut MCP Server MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Jsoncut MCP Server connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Jsoncut MCP Server MCP to TypingMind?

Jsoncut MCP Server runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Jsoncut MCP Server MCP provide in TypingMind?

Jsoncut MCP Server exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Jsoncut MCP Server MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Jsoncut MCP Server requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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