Chart Canvas

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!

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 databases
• CSV (.csv) - Comma-separated values
• Parquet (.parquet) - Columnar storage format
• JSON (.json) - JSON arrays of objects
• NDJSON (.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-mcp

Or use directly with npx (no installation needed):

npx @gluip/chart-canvas-mcp

Configuration

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

1. Start your MCP client (e.g., Claude Desktop)
2. The server will automatically start on port 3000
3. Use the showCanvas tool to open the dashboard in your browser
4. Ask 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 series
• bar - Bar charts for comparisons
• scatter - Scatter plots for data distribution
• pie - Pie charts with labels
• table - Data tables with headers
• flowchart - 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.sqlite or 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 file
• query - SQL SELECT query (read-only)
• visualizationType - Type of chart: line, bar, scatter, pie, or table
• columnMapping (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 visualization
• description - Optional description
• useColumnAsXLabel - 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:prod

MCP 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

Links

• NPM Package
• GitHub Repository
• Model Context Protocol