mcp -chart

This MCP server provides an extensive chart generation service with a wide range of chart types and strong type safety via Zod schemas. It exposes a suite of charting tools that can be invoked as MCP endpoints to generate data for bar, line, pie, doughnut, radar, polarArea, scatter, bubble, OHLC, violin, gauge, radialGauge, progress, sparkline, and sankey visualizations. You can run the server locally and then call the chart tool corresponding to the desired chart type; input configurations are validated against the chart-specific schemas to ensure correct data formatting before generating charts. For Claude.AI users, public endpoints are available for immediate use without local setup: Streamable HTTP at https://chart.mcp.cloudcertainty.com/mcp and Server-Sent Events at https://chart.mcp.cloudcertainty.com/sse.

Prerequisites:

• Node.js (v14+ recommended) and npm
• Git

1. Clone the repository git clone https://github.com/KamranBiglari/mcp-server-chart.git cd mcp-server-chart

2. Install dependencies npm install

3. Build the project (if applicable) npm run build

4. Start the server locally npm start

5. Optional: configure your MCP client to connect to the local server (see configuration section in README for examples). If using Claude.AI integration, you can also use the provided public endpoints directly without running a local instance.

Tips and notes:

• The server supports 15+ chart types with Zod-based validation to ensure your chart configurations match the expected schema.
• Public Claude.AI endpoints are available for quick experimentation without local setup.
• If you customize locally, ensure your input JSON conforms to the supported data formats per chart type (see the Chart Type Reference in the README).
• Use the MCP client configuration example to wire the server into your MCP client by specifying either a local Node path or a URL for Claude.AI integration.
• Common issues include invalid input formats or missing required fields for a chart type; use the schema validation to catch these early.
• Environment variables like PORT or NODE_ENV can be used to customize deployment behavior.
• For production deployments, consider serving behind a reverse proxy and enabling appropriate security measures around endpoints.