makechartswithai

makechartswithai

MCP server that enables AI agents to recommend and generate charts from datasets using natural language, supporting 46 chart types and multiple output formats.

Category
Visit Server

README

Make Charts With AI — MCP Server

The Make Charts With AI MCP server exposes AI-powered chart recommendation and generation capabilities as standard Model Context Protocol (MCP) tools for AI agents — Claude Desktop, Cursor, Antigravity, VS Code, and more.

npm package: makechartswithai


⚡ Quick Start

1. Get an API Key

Visit makechartswithai.online/mcp and generate a free anonymous API key (5 tool calls per key).

2. Add to Your MCP Client

Add the following to your MCP client configuration file (claude_desktop_config.json, mcp_config.json, etc.):

{
  "mcpServers": {
    "makechartswithai": {
      "command": "npx",
      "args": ["-y", "makechartswithai"],
      "env": {
        "MAKECHARTSWITHAI_API_KEY": "mcwai_ak_your_key_here",
        "MAKECHARTSWITHAI_BACKEND_URL": "https://makechartswithai.online"
      }
    }
  }
}

That's it — your AI agent can now recommend and generate charts.


🛠️ Available MCP Tools

1. recommend_charts

Analyzes raw datasets (JSON, CSV, Markdown tables, or natural language prompts) and suggests optimal chart types with confidence scores and rationale.

Inputs:

  • dataset_json (string, optional): Raw JSON data array or object string.
  • dataset_content (string, optional): CSV, Markdown table, or plain text data.
  • user_intent (string, optional): Natural language explanation of what you want to visualize.
  • focus_columns (string[], optional): Target column names to prioritize.

2. generate_chart

Generates a production-ready chart specification and returns the output in your choice of format (share_link, svg, png). Rendering engine (vegalite, echarts, chartjs) is automatically determined based on chart type capabilities.

Inputs:

  • dataset_json (string, optional): Raw JSON data string.
  • dataset_content (string, optional): Tabular dataset (CSV, Markdown).
  • user_intent (string, optional): Natural language visualization prompt.
  • preferred_chart_type (string enum, optional): One of 46 supported chart types (Bar Chart, Grouped Bar Chart, Stacked Bar Chart, Line Chart, Scatter Plot, Pie Chart, Area Chart, Heatmap, Radar Chart, Sunburst Chart, Sankey Diagram, etc.).
  • output_format (enum, optional: "share_link" | "svg" | "png", default: "share_link"):
    • "share_link": Returns public view and edit URLs on makechartswithai.online.
    • "svg": Renders and returns vector SVG XML markup string.
    • "png": Renders and returns high-res PNG image payload (image/png base64 + saved file artifact).

🔑 Environment Variables

Variable Required Description
MAKECHARTSWITHAI_API_KEY Your API key from makechartswithai.online/mcp
MAKECHARTSWITHAI_BACKEND_URL No Backend URL (defaults to https://makechartswithai.online)

Legacy aliases: CHARTIT_API_KEY and CHARTIT_BACKEND_URL are also supported for backward compatibility.


📊 Supported Chart Types (46)

Area Chart, Bar Chart, Bar Table, Boxplot, Bubble Chart, Bullet Chart, Bump Chart, Calendar Heatmap, Candlestick Chart, Choropleth, Combo Chart, Connected Scatter Plot, Density Plot, Doughnut Chart, ECDF Plot, Funnel Chart, Gantt Chart, Gauge Chart, Grouped Bar Chart, Heatmap, Histogram, KPI Card, Line Chart, Lollipop Chart, Map, Network Graph, Parallel Coordinates, Pie Chart, Pyramid Chart, Radar Chart, Range Area Chart, Ranged Dot Plot, Regression, Rose Chart, Sankey Diagram, Scatter Plot, Slope Chart, Sparkline, Stacked Bar Chart, Streamgraph, Strip Plot, Sunburst Chart, Tree, Treemap, Violin Plot, Waterfall Chart.


🔗 Links

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured