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": [
"quick-chart-mcp@1.0.13"
],
"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.
Related Servers
MCPAgent
An intelligent agent framework based on MCP, supporting multiple large language models and tool integrations for testing single-agent effectiveness.
Makefile MCP Server
Exposes Makefile targets as callable tools for AI assistants.
Sentry MCP Server
An MCP server for interacting with the Sentry error tracking and performance monitoring platform.
Semgrep
Enable AI agents to secure code with Semgrep.
Python Notebook MCP
Enables AI assistants to interact with local Jupyter notebooks (.ipynb).
Gemsuite
The ultimate open-source server for advanced Gemini API interaction with MCP, intelligently selects models.
MCP CLI
A command-line interface for interacting with Model Context Protocol servers.
EOL MCP Server
Check software end-of-life (EOL) dates and support status using the endoflife.date API to provide accurate lifecycle and security information.
JavaScript Executor MCP Server
Execute JavaScript code in a modern runtime environment with support for various built-in modules.
UML-MCP
A diagram generation server supporting multiple UML and other diagram types, with various output formats. It integrates with rendering services like Kroki and PlantUML.
