Chart Canvas MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Chart Canvas MCP ServerCreate a line chart of monthly sales for 2024"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Chart Canvas MCP Server
Interactive visualization dashboard for AI assistants via Model Context Protocol
Create beautiful charts, diagrams, and tables directly from your AI conversations. Chart Canvas provides a real-time dashboard that displays visualizations as you work with LLMs like Claude.
Demo

Watch the full demo on YouTube to see Chart Canvas in action!
Related MCP server: MCP Data Visualization Server
Features
✨ Multiple Chart Types: Line, bar, scatter, pie charts, tables, and Mermaid diagrams
🎨 Interactive Dashboard: Drag-and-drop grid layout with real-time updates
🔄 Live Synchronization: Changes appear instantly in your browser
📊 Rich Visualizations: Powered by ECharts and Mermaid
💾 Universal Data Sources: Query SQLite, CSV, Parquet, JSON, and NDJSON files directly
⚡ Smart Data Flow: Execute queries server-side - data stays local, never sent to LLM
🔒 Privacy First: Your data never leaves your machine
🚀 Easy Setup: One command to get started
🌐 Production Ready: Built-in production mode with optimized builds
Supported Data Sources
Chart Canvas can query and visualize data from multiple file formats:
SQLite (
.db,.sqlite,.sqlite3) - Relational databasesCSV (
.csv) - Comma-separated valuesParquet (
.parquet) - Columnar storage formatJSON (
.json) - JSON arrays of objectsNDJSON (
.jsonl,.ndjson) - Newline-delimited JSON
Privacy & Performance: All queries execute locally on your machine using DuckDB. Query results are transformed into visualizations server-side - only metadata (chart configuration) is sent to the LLM, never your actual data. This makes it fast, scalable, and private.
Quick Start
Installation
npm install -g @gluip/chart-canvas-mcpOr use directly with npx (no installation needed):
npx @gluip/chart-canvas-mcpConfiguration
Add to your MCP client configuration (e.g., Claude Desktop):
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"chart-canvas": {
"command": "npx",
"args": ["-y", "@gluip/chart-canvas-mcp"]
}
}
}Usage
Start your MCP client (e.g., Claude Desktop)
The server will automatically start on port 3000
Use the
showCanvastool to open the dashboard in your browserAsk the AI to create visualizations!
Example Prompts
"Show me a line chart comparing sales data for 2023 and 2024"
"Create a pie chart showing market share by region"
"Draw a flowchart for the user authentication process"
"Make a table with team member information"
"Show me the database schema for my SQLite database"
"Query the athletes table and show the top 10 with most personal records"
"Create a chart showing sales trends from the database grouped by region"MCP Tools
addVisualization
Create charts, diagrams, and tables on the canvas.
Supported Types:
line- Line charts with multiple seriesbar- Bar charts for comparisonsscatter- Scatter plots for data distributionpie- Pie charts with labelstable- Data tables with headersflowchart- Mermaid diagrams (flowcharts, sequence diagrams, Gantt charts, etc.)
Example:
{
type: "line",
title: "Monthly Sales",
series: [
{ name: "2023", data: [[1, 120], [2, 132], [3, 101]] },
{ name: "2024", data: [[1, 220], [2, 182], [3, 191]] }
],
xLabels: ["Jan", "Feb", "Mar"]
}removeVisualization
Remove a specific visualization by ID.
clearCanvas
Remove all visualizations from the canvas.
showCanvas
Open the dashboard in your default browser.
getDatabaseSchema
Inspect the structure of a SQLite database to understand available tables and columns before writing queries.
Parameters:
databasePath- Path to SQLite database file (e.g.,./data/mydb.sqliteor absolute path)
Example:
{
databasePath: "/path/to/database.db";
}Returns: Formatted schema showing all tables, columns, data types, and constraints.
queryAndVisualize
Execute a SQL query on a SQLite database and create a visualization from the results. Queries are executed server-side and must be read-only (SELECT only). Maximum 10,000 rows.
Parameters:
databasePath- Path to SQLite database filequery- SQL SELECT query (read-only)visualizationType- Type of chart:line,bar,scatter,pie, ortablecolumnMapping(optional for table) - Mapping of columns to chart axes:xColumn- Column for X-axis (required for charts)yColumns- Array of columns for Y-axis (required for charts)seriesColumn- Column to group data into separate series (optional)groupByColumn- Alternative grouping column (optional)
title- Optional title for visualizationdescription- Optional descriptionuseColumnAsXLabel- If true, use X column values as labels instead of numbers
Example:
{
databasePath: "./data/sales.db",
query: "SELECT region, SUM(revenue) as total FROM sales GROUP BY region",
visualizationType: "bar",
columnMapping: {
xColumn: "region",
yColumns: ["total"]
},
title: "Revenue by Region",
useColumnAsXLabel: true
}Security: Only SELECT and WITH (CTE) queries are allowed. INSERT, UPDATE, DELETE, DROP, and other modifying operations are blocked.
Architecture
Backend: Node.js + TypeScript + Express + MCP SDK
Frontend: Vue 3 + ECharts + Mermaid + Grid Layout
Communication: Real-time polling for instant updates
Development
Local Development
# Clone repository
git clone https://github.com/gluip/chart-canvas.git
cd chart-canvas
# Install backend dependencies
cd backend
npm install
# Install frontend dependencies
cd ../frontend
npm install
# Development mode (backend + frontend separate)
# Terminal 1 - Backend
cd backend
npm run dev
# Terminal 2 - Frontend
cd frontend
npm run dev
# Production mode (single server)
cd backend
npm run build:all
npm run start:prodMCP Configuration for Local Development
{
"mcpServers": {
"chart-canvas": {
"command": "/path/to/node",
"args": [
"/path/to/chart-canvas/backend/node_modules/.bin/tsx",
"/path/to/chart-canvas/backend/src/index.ts"
]
}
}
}License
MIT © 2026 Martijn
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