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.

Related MCP server: ECharts MCP Server

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 configuration examples for common clients.

Claude Desktop

Add to your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "highchart-mcp-server": {
      "command": "node",
      "args": ["/absolute/path/to/highchart-mcp-server/dist/index.js"],
      "env": {
        "TRANSPORT": "stdio",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Cursor / VS Code

Add to .cursor/mcp.json or .vscode/mcp.json in your project root:

{
  "mcpServers": {
    "highchart-mcp-server": {
      "command": "node",
      "args": ["/absolute/path/to/highchart-mcp-server/dist/index.js"],
      "env": {
        "TRANSPORT": "stdio",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Streamable HTTP (Network Clients)

For clients that connect over the network, start the server with the HTTP transport:

TRANSPORT=streamable-http PORT=3000 node dist/index.js

Then connect your MCP client to http://localhost:3000/mcp.

Available Tools

Basic Chart Call

Chart generation is done by invoking MCP tools with chart config objects. For example:

{
  "tool": "create_chart",
  "input": {
    "type": "line",
    "data": {
      "categories": ["Jan", "Feb", "Mar"],
      "series": [
        {"name": "Sales", "data": [10, 15, 20]}
      ]
    },
    "options": {}
  }
}

The MCP server validates the input and returns chart data or export artifacts.

6. Validation System

The server uses a schema validation system:

• Zod or JSON Schema definitions for each chart type

• Shared utility schemas for common entities (axis, series, color)

• Error objects with detailed messages

Invalid requests return structured responses indicating the validation failures.

7. Production Readiness

Deployment

Supports deployment in production environments:

• Containerized deployment with Docker

• Kubernetes configuration with health probes

• Environment variable configuration

Security

• Configurable auth providers

• TLS/HTTPS enforcement

• Audit logs

Scalability

• Stateless MCP server instances

• Horizontal autoscaling

• Caching for repeated chart requests

8. AI & LLM Integration

The server provides AI‑centric features:

• Natural language parser — Converts descriptions to chart configs

• Suggestions API — Suggests chart types and options

• Error correction API — Uses AI to fix configs

These integrations make the server suitable for interactive AI applications and assistants.

9. Tooling & Developer Experience

CLI

Provides commands for:

• Validating chart configs

• Exporting charts locally

• Testing chart output

SDKs

• JavaScript SDK for client integrations

• Python SDK for backend integrations

Playground

An interactive UI to test chart configs and see live output.

10. Analytics & Monitoring

• Prometheus metrics exposed

• Logs with structured levels

• Dashboard templates for Grafana

These help monitor server performance and usage patterns.

11. Contributing

To contribute:

1. Fork the repository

2. Create a feature branch

3. Add tests and documentation

4. Submit a pull request

Please follow code style guidelines and include tests for new features.

12. License

Released under the MIT License.