Quick Chart MCP Server
A server for creating charts and visualizations using the Quick Chart API.
Quick Chart MCP Server
A Model Context Protocol (MCP) server that provides chart tools, allowing it to interact with the quick chart through a standardized interface. This implementation is based on the chart definition and enables users can open quick chart pages seamlessly.
Overview
This MCP server tools:
- Interact with Quick Chart
The server implements the Model Context Protocol specification to standardize chart interactions for AI agents.
Prerequisites
- Node.js (v16 or higher)
- pnpm (recommended), npm, or yarn
Installation
Installing via Smithery
To install quick-chart-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @datafe/quick-chart-mcp --client claude
Option 1: Install from npm (recommend for clients like Cursor/Cline)
# Install globally
npm install -g quick-chart-mcp
# Or install locally in your project
npm install quick-chart-mcp
Option 2: Build from Source (for developers)
- Clone this repository:
git clone https://github.com/datafe/quick-chart-mcp
cd quick-chart-mcp
- Install dependencies (pnpm is recommended, npm is supported):
pnpm install
- Build the project:
pnpm run build
- Development the project (by @modelcontextprotocol/inspector):
pnpm run dev
Configuration
MCP Configs
{
"mcpServers": {
"quick-chart-mcp": {
"autoApprove": [],
"disabled": false,
"timeout": 300,
"command": "npx",
"args": [
"[email protected]"
],
"transportType": "stdio"
}
}
}
Environment Setup
Create a .env file with your credentials:
# Quick Chart Configuration
NODE_ENV=optional_development_or_product
QUICK_CHART_DRAW_URL=optional_quick_chart_draw_url
NEED_INSTALL_QUICK_CHART=optional_true_or_false
Project Structure
quick-chart-mcp/
├── src/
│ ├── index.ts # Main entry point
├── package.json
└── tsconfig.json
Available Tools
The MCP server provides the following Quick Chart tools:
GetChartImgLink- Retrieve chart image link by parameters.InstallQuickChart- Install quick chart service locally.
Security Considerations
- Use environment variables for sensitive information
- Regularly monitor and audit AI agent activities
Troubleshooting
If you encounter issues:
- Verify the build was successful
Dependencies
image APIs.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
License
This project is licensed under the MIT License.
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