Jsoncut MCP Server▌
by jsoncut
Jsoncut MCP Server: a json image generator and image generation API for programmatic video generation and dynamic image
Enables AI agents to generate JSON configurations for creating images and videos programmatically through the Jsoncut API, with support for layers, positioning, transitions, and validation.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers building automated visual content generation
- / Creating programmatic image and video workflows
- / AI agents generating multimedia content
capabilities
- / Generate JSON configs for image composition
- / Create JSON configs for video rendering
- / Validate configurations against Jsoncut API
- / Access JSON schemas as MCP resources
- / Configure layers, positioning, and transitions
what it does
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.
about
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.
how to install
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.
license
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.
readme
[!WARNING] ⚠️ OUTDATED REPOSITORY - This repository is no longer maintained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
<p align="center"> <img src="assets/logo.png" alt="Jsoncut Logo" width="200"/> </p> <h1 align="center">Jsoncut MCP Server</h1> <p align="center"> <strong>Model Context Protocol server for the Jsoncut API</strong><br> Enable AI agents to generate stunning images and videos programmatically </p> <p align="center"> <a href="https://www.npmjs.com/package/@jsoncut/mcp-server"><img src="https://img.shields.io/npm/v/@jsoncut/mcp-server.svg" alt="npm version"></a> <a href="https://github.com/jsoncut/jsoncut-mcp-server/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a> </p>
🚀 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)
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:
{
"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:
# 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 API Key
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
🎯 MCP Client Configuration
Remote Server (Recommended)
Use the public server at https://mcp.jsoncut.com:
Cursor IDE
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"
}
}
}
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
{
"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
{
"jsoncut": {
"command": "npx",
"args": ["-y", "@jsoncut/mcp-server"],
"env": {
"JSONCUT_API_KEY": "your_api_key_here"
}
}
}
Claude Desktop
{
"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
{
"jsoncut": {
"url": "http://localhost:3210/mcp",
"headers": {
"X-API-Key": "your_jsoncut_api_key_here"
}
}
}
Claude Desktop
{
"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 schemaschema://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:
{
"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:
{
"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 validateapiKey: 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
- Create Configuration: Use
create_image_configorcreate_video_config - Validate (optional): Call
validate_configif you have actual file paths - 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:
export JSONCUT_API_KEY=your_api_key_here
npm run inspector
🔧 Development
Local Development
# 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:
{
"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 typesvideo-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
- API Documentation: docs.jsoncut.com
- Website: jsoncut.com
- GitHub: github.com/jsoncut/jsoncut-mcp-server
- npm: @jsoncut/mcp-server
- Support: [email protected]
<p align="center"> Built with the <a href="https://github.com/modelcontextprotocol">Model Context Protocol SDK</a> by Anthropic </p> <p align="center"> Made with ❤️ by the Jsoncut Team </p>
FAQ
- What is the Jsoncut MCP Server MCP server?
- Jsoncut MCP Server is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Jsoncut MCP Server?
- This profile displays 59 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★59 reviews- ★★★★★Ama Iyer· Dec 24, 2024
Jsoncut MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Shikha Mishra· Dec 16, 2024
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★James Gill· Dec 8, 2024
Jsoncut MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Li Martinez· Dec 4, 2024
Jsoncut MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Aanya Reddy· Nov 27, 2024
According to our notes, Jsoncut MCP Server benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Mei Anderson· Nov 23, 2024
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Mei Ndlovu· Nov 15, 2024
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Rahul Santra· Nov 7, 2024
Jsoncut MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Pratham Ware· Oct 26, 2024
Jsoncut MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Aanya Singh· Oct 18, 2024
Jsoncut MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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