JSON Canvas MCP Server
A Model Context Protocol server that enables creating, modifying, and validating infinite canvas data structures according to the JSON Canvas 1.0 specification.
Cam10001110101
README
JSON Canvas MCP Server
A Model Context Protocol (MCP) server implementation that provides tools for working with JSON Canvas files according to the official specification. This server enables creating, modifying, and validating infinite canvas data structures.
Overview
The JSON Canvas MCP server provides a complete implementation of the JSON Canvas 1.0 specification, enabling:
- Creation and manipulation of infinite canvas data
- Support for all node types (text, file, link, group)
- Edge connections with styling and labels
- Validation against the specification
- Configurable output paths
Components
Resources
The server exposes the following resources:
canvas://schema: JSON Schema for validating canvas filescanvas://examples: Example canvas files demonstrating different featurescanvas://templates: Templates for creating new canvases
Tools
Node Operations
-
create_node
- Create a new node of any supported type
- Input:
type(string): Node type ("text", "file", "link", "group")properties(object): Node-specific properties- Common:
id,x,y,width,height,color - Type-specific:
text,file,url, etc.
- Common:
- Returns: Created node object
-
update_node
- Update an existing node's properties
- Input:
id(string): Node ID to updateproperties(object): Properties to update
- Returns: Updated node object
-
delete_node
- Remove a node and its connected edges
- Input:
id(string): Node ID to delete
- Returns: Success confirmation
Edge Operations
-
create_edge
- Create a new edge between nodes
- Input:
id(string): Unique edge identifierfromNode(string): Source node IDtoNode(string): Target node IDfromSide(optional string): Start side ("top", "right", "bottom", "left")toSide(optional string): End sidecolor(optional string): Edge colorlabel(optional string): Edge label
- Returns: Created edge object
-
update_edge
- Update an existing edge's properties
- Input:
id(string): Edge ID to updateproperties(object): Properties to update
- Returns: Updated edge object
-
delete_edge
- Remove an edge
- Input:
id(string): Edge ID to delete
- Returns: Success confirmation
Canvas Operations
-
validate_canvas
- Validate a canvas against the specification
- Input:
canvas(object): Canvas data to validate
- Returns: Validation results with any errors
-
export_canvas
- Export canvas to different formats
- Input:
format(string): Target format ("json", "svg", "png")canvas(object): Canvas data to export
- Returns: Exported canvas in requested format
Usage with Claude Desktop
Docker
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"jsoncanvas": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-v",
"canvas-data:/data",
"mcp/jsoncanvas"
],
"env": {
"OUTPUT_PATH": "/data/output"
}
}
}
}
UV
{
"mcpServers": {
"jsoncanvas": {
"command": "uv",
"args": [
"--directory",
"/path/to/jsoncanvas",
"run",
"mcp-server-jsoncanvas"
],
"env": {
"OUTPUT_PATH": "./output"
}
}
}
}
Configuration
The server can be configured using environment variables:
OUTPUT_PATH: Directory where canvas files will be saved (default: "./output")FORMAT: Default output format for canvas files (default: "json")
Building
Docker Build
docker build -t mcp/jsoncanvas .
Local Build
# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -e .
# Run tests
pytest
Example Usage
Creating a Canvas
from jsoncanvas import Canvas, TextNode, Edge
# Create nodes
title = TextNode(
id="title",
x=100,
y=100,
width=400,
height=100,
text="# Hello Canvas\n\nThis is a demonstration.",
color="#4285F4"
)
info = TextNode(
id="info",
x=600,
y=100,
width=300,
height=100,
text="More information here",
color="2" # Using preset color
)
# Create canvas
canvas = Canvas()
canvas.add_node(title)
canvas.add_node(info)
# Connect nodes
edge = Edge(
id="edge1",
from_node="title",
to_node="info",
from_side="right",
to_side="left",
label="Connection"
)
canvas.add_edge(edge)
# Save canvas
canvas.save("example.canvas")
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
@kazuph/mcp-taskmanager
Model Context Protocol server for Task Management. This allows Claude Desktop (or any MCP client) to manage and execute tasks in a queue-based system.
Apple MCP Server
Enables interaction with Apple apps like Messages, Notes, and Contacts through the MCP protocol to send messages, search, and open app content using natural language.
Claude Code MCP
An implementation of Claude Code as a Model Context Protocol server that enables using Claude's software engineering capabilities (code generation, editing, reviewing, and file operations) through the standardized MCP interface.
MCP Package Docs Server
Facilitates LLMs to efficiently access and fetch structured documentation for packages in Go, Python, and NPM, enhancing software development with multi-language support and performance optimization.
Todoist MCP
An MCP server that enables LLMs to interact with Todoist tasks, projects, and other features through the Todoist API.
Notion API MCP Server
Enables advanced todo list management and content organization using Notion's API, supporting features like creating databases, dynamic filtering, and collaborative task tracking.
Linear MCP Server
A Model Context Protocol server that integrates with Linear's issue tracking system, allowing LLMs to create, update, search, and comment on Linear issues through natural language interactions.
Sequential Thinking MCP Server
This server facilitates structured problem-solving by breaking down complex issues into sequential steps, supporting revisions, and enabling multiple solution paths through full MCP integration.