JSON Canvas MCP Server
A server for creating, modifying, validating, and exporting JSON Canvas files, a format for infinite canvas data.
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.
관련 서버
Yandex Tracker
Integrates with Yandex Tracker, allowing an AI assistant to interact with its task management system via the MCP protocol.
Evernote
Connects your Evernote account to an LLM, enabling natural language search and queries over your notes.
Screen View
Capture and analyze screenshots using the Claude Vision API.
EmoVideoAna
analytics emotion of video
Notion
Manage and interact with your entire Notion workspace.
Adspirer Ads Manager
Manage digital advertising campaigns with AI-powered insights from Adspirer.com.
ChatExcel
A powerful server for Excel file processing, data analysis, and visualization, leveraging Python and Go for high performance.
Google Docs
An MCP server for integrating with and managing Google Docs.
Superthread MCP Extended
A perfect drop-in replacement to the official Superthread MCP, providing way more tools. Cloudflare Workers based Remote MCP server
physbound
PhysBound is a specialized "Physics Linter" for AI that deterministically validates RF and thermodynamic claims against hard physical limits, preventing hallucinations in engineering workflows.