by jsoncut
Jsoncut MCP Server: a json image generator and image generation API for programmatic video generation and dynamic image
Generates JSON configurations for programmatic image and video creation through the Jsoncut API. Includes validation and schema resources for building visual content with layers, transitions, and effects.
Jsoncut MCP Server is an official MCP server published by jsoncut that provides AI assistants with tools and capabilities via the Model Context Protocol. Jsoncut MCP Server: a json image generator and image generation API for programmatic video generation and dynamic image It is categorized under developer tools, design.
You can install Jsoncut MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Jsoncut MCP Server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Jsoncut MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Jsoncut MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Jsoncut MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
According to our notes, Jsoncut MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Jsoncut MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Jsoncut MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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[!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/mcpFor more information, see: https://docs.jsoncut.com/docs/mcp/overview
export JSONCUT_API_KEY=your_api_key_here
npx -y @jsoncut/mcp-server
A public MCP server is available at https://mcp.jsoncut.com. No installation needed - just configure your MCP client with your API key:
{
"jsoncut": {
"url": "https://mcp.jsoncut.com/mcp",
"headers": {
"x-api-key": "your_jsoncut_api_key_here"
}
}
}
You can also run your own local server using Docker:
# 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:
# Start the service
docker-compose up -d
# Access at: http://localhost:3210/mcp
📖 See DOCKER.md for complete Docker deployment guide
Get your Jsoncut API key at jsoncut.com
# 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
Use the public server at https://mcp.jsoncut.com:
Open Cursor Settings → Features → MCP Servers → "+ Add New MCP Server"
{
"jsoncut": {
"url": "https://mcp.jsoncut.com/mcp",
"headers": {
"X-API-Key": "your_jsoncut_api_key_here"
}
}
}
Add to your claude_desktop_config.json:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"jsoncut": {
"url": "https://mcp.jsoncut.com/mcp",
"headers": {
"X-API-Key": "your_jsoncut_api_key_here"
}
}
}
}
For local development without network access:
{
"jsoncut": {
"command": "npx",
"args": ["-y", "@jsoncut/mcp-server"],
"env": {
"JSONCUT_API_KEY": "your_api_key_here"
}
}
}
{
"mcpServers": {
"jsoncut": {
"command": "npx",
"args": ["-y", "@jsoncut/mcp-server"],
"env": {
"JSONCUT_API_KEY": "your_api_key_here"
}
}
}
}
If you're running your own local Docker server:
{
"jsoncut": {
"url": "http://localhost:3210/mcp",
"headers": {
"X-API-Key": "your_jsoncut_api_key_here"
}
}
}
{
"mcpServers": {
"jsoncut": {
"url": "http://localhost:3210/mcp",
"headers": {
"X-API-Key": "your_jsoncut_api_key_here"
}
}
}
}
The server automatically exposes JSON schemas as MCP resources:
schema://image - Complete image generation schemaschema://video - Complete video generation schemaAI agents can read these directly without tool calls for fast access to all configuration options.
create_image_configCreate JSON configurations for image generation with a layer-based system.
Layer Types:
Positioning:
{ x: 100, y: 50 }center, top, bottom, top-left, top-right, etc.{ x: 0.5, y: 0.5, originX: "center", originY: "center" }Example:
{
"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_configCreate JSON configurations for video generation with clips, layers, and transitions.
Key Features:
Example:
{
"width": 1920,
"height": 1080,
"fps": 30,
"defaults": {
"duration": 3,
"transition": { "name": "fade", "duration": 1 }
},
"clips": [
{
"layers": [
{ "type": "title", "text": "Welcome", "position": "center" }
]
}
]
}
validate_configValidate configurations against the Jsoncut API before submission.
Parameters:
type: "image" or "video"config: Configuration object to validateapiKey: Optional API key (uses environment if not provided)Returns:
get_image_schema / get_video_schemaGet 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.
create_image_config or create_video_configvalidate_config if you have actual file pathsThe schemas are automatically available as MCP resources, so AI agents have instant access to all configuration options.
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:
Use the MCP Inspector for interactive testing:
export JSONCUT_API_KEY=your_api_key_here
npm run inspector
# 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
For Cursor/Claude Desktop, use the local build:
{
"jsoncut": {
"command": "node",
"args": ["/absolute/path/to/jsoncut-mcp-server/dist/index.js"],
"env": {
"JSONCUT_API_KEY": "your_api_key_here"
}
}
}
See the examples/ directory for complete configurations:
image-example.json - Image generation with multiple layer typesvideo-example.json - Video generation with clips and transitionsContributions are welcome! Please feel free to submit a Pull Request.
MIT License - see LICENSE file for details.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.