Highcharts MCP Server

A Model Context Protocol (MCP) server designed to generate Highcharts‑based charts and visualizations in a production‑ready, AI‑enhanced, and validated manner. This server can be integrated with any MCP‑capable AI client (such as Claude, ChatGPT, Cursor, Dify, VSCode, etc.) to automate chart generation, validation, and export workflows.

Overview

The Highcharts MCP Server provides:

• Chart generation for all common Highcharts types
• Schema validation of chart configurations
• Production grade features for scalability, security, and monitoring
• AI‑assisted functionality, such as natural language chart generation
• CLI and SDK tooling for developers

It acts as an MCP tool suite exposing Highcharts chart generation endpoints that can be invoked by AI assistants or custom clients.

Table of Contents

1. Features
2. Architecture
3. Installation
4. AI Client Integration
5. Usage
6. Validation System
7. Production Readiness
8. AI & LLM Integration
9. Tooling & Developer Experience
10. Analytics & Monitoring
11. Contributing
12. License

1. Features

Core Chart Capabilities

The MCP server supports generating charts using Highcharts configurations:

• Line, Column, Bar
• Area, Scatter, Pie
• Gauge, Treemap, Heatmap
• Financial charts (OHLC, Candlestick)
• Specialized chart types (Sankey, Network, Timeline)
• Export to PNG, SVG, PDF, and interactive HTML bundles (Concepts based on MCP chart servers and Highcharts Node export support)([highcharts.com][2])

Schema Validation

All incoming chart configuration requests are validated against JSON schema or Zod schemas:

• Type enforcement for chart options
• Validation of data arrays, axis definitions, series structure
• Detailed and structured error responses on invalid configs (Validation approach inspired by existing MCP chart servers)([GitHub][3])

Production‑Grade Reliability

• Scalable deployment via Docker, Kubernetes
• Multi‑transport support: HTTP(S), SSE, Streamable HTTP, STDIO
• Health check endpoints
• High availability and load balancing
• Configurable caching and rate limits

Security & Access Control

• API key, OAuth2, and JWT authentication
• RBAC (Role‑based access control)
• Rate limiting and quotas
• Audit logging for compliance

AI & LLM Enhancements

• Natural language to chart config translation
• Chart type suggestions based on dataset or analytics question
• Auto‑correction of invalid configs using AI
• Integration hooks for LLM workflows

Export & Output

• Static and interactive chart outputs
• Batch and scheduled export capabilities
• Link generation for shareable chart assets

Monitoring & Observability

• Prometheus / Grafana integration
• Structured logging
• Performance metrics
• Error reporting dashboards

Tooling & SDKs

• CLI tooling for quick local operations
• JavaScript and Python SDKs
• Interactive schema documentation
• Playground UI for testing configurations

2. Architecture

The server consists of:

• Transport layer — Handles different MCP transports (HTTP/SSE, streamable, STDIO)
• Schema validation layer — Ensures chart configs are correct
• Chart renderer — Uses Highcharts engine (Node export or server rendering)
• AI processing layer — Optional natural language preprocessing
• Output services — Export and sharing

The server uses a modular structure to isolate responsibilities and enable plug‑ins/extensions.

3. Installation

Prerequisites

• Node.js 18+
• Docker (optional)
• Kubernetes (optional)

Local Setup

Clone the repository:

git clone https://your-repo-url
cd highcharts-mcp-server
npm install
npm run build
npm start

Docker

Build the Docker image:

docker build -t highcharts-mcp-server .
docker run -p 8080:8080 highcharts-mcp-server

4. AI Client Integration

This MCP server works with any MCP-capable AI client. Below are configu

... View full README on GitHub