AntV Chart

The MCP Server Chart allows you to generate charts using the AntV library.

It’s a TypeScript-based MCP server that lets you create over 15 different chart types directly from MCP-compatible applications like Claude, VSCode, or Dify.

Features

  • 📊 15+ Chart Types – Area, bar, column, pie, line, scatter, radar, treemap, histogram, and specialized charts like fishbone diagrams and mind maps
  • 🔗 Direct Image URLs – Every chart generation returns a ready-to-use image URL that can be embedded anywhere
  • Multiple Transport Options – Supports stdio, SSE, and streamable protocols for different deployment needs
  • 🌐 Cross-Platform Compatibility – Works with Claude Desktop, VSCode, Cline, Cherry Studio, and other MCP-compatible applications
  • 🏢 Private Deployment Support – Customize chart generation service with your own backend infrastructure
  • 🎛️ Flexible Configuration – Command-line options for transport protocol, port, and endpoint customization

Use Cases

  • Data Analysis Reports – Generate charts from CSV data or statistical analysis results during research conversations with AI assistants
  • Business Intelligence Dashboards – Create quick visualizations from business metrics and KPIs without leaving your AI workflow
  • Educational Content Creation – Build charts for tutorials, presentations, or educational materials while working with AI on content development
  • Software Documentation – Generate flowcharts, network diagrams, and architectural visualizations for technical documentation projects

How to Use It

Desktop Application Setup

For Claude Desktop, VSCode, Cline, or other MCP-compatible applications, add this configuration:

Mac/Linux:

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "npx",
      "args": [
        "-y",
        "@antv/mcp-server-chart"
      ]
    }
  }
}

Windows:

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "@antv/mcp-server-chart"
      ]
    }
  }
}

Server Transport Options

Install globally for HTTP/SSE access:

npm install -g @antv/mcp-server-chart

Run with different transport protocols:

# SSE transport (default endpoint: /sse)
mcp-server-chart --transport sse
# Streamable transport (default endpoint: /mcp)
mcp-server-chart --transport streamable

Access endpoints:

  • SSE: http://localhost:1122/sse
  • Streamable: http://localhost:1122/mcp

Available Chart Generation Tools

  • generate_area_chart – Creates area charts for trend visualization
  • generate_bar_chart – Horizontal bar charts for categorical comparisons
  • generate_column_chart – Vertical column charts for data comparison
  • generate_dual_axes_chart – Charts with two Y-axes for different data scales
  • generate_fishbone_diagram – Cause-and-effect analysis diagrams
  • generate_flow_diagram – Process flow and workflow charts
  • generate_histogram_chart – Distribution analysis charts
  • generate_line_chart – Time series and trend line charts
  • generate_mind_map – Hierarchical mind mapping diagrams
  • generate_network_graph – Network relationship visualizations
  • generate_pie_chart – Proportion and percentage charts
  • generate_radar_chart – Multi-dimensional data comparison
  • generate_scatter_chart – Correlation and distribution plots
  • generate_treemap_chart – Hierarchical data visualization
  • generate_word_cloud_chart – Text frequency visualizations

CLI Configuration Options

  • --transport, -t – Protocol type: “stdio”, “sse”, or “streamable” (default: stdio)
  • --port, -p – Port number for HTTP transports (default: 1122)
  • --endpoint, -e – Custom endpoint path (SSE: “/sse”, Streamable: “/mcp”)
  • --help, -h – Display help information

Private Deployment

For custom chart generation services, set the VIS_REQUEST_SERVER environment variable:

{
  "mcpServers": {
    "mcp-server-chart": {
      "command": "npx",
      "args": ["-y", "@antv/mcp-server-chart"],
      "env": {
        "VIS_REQUEST_SERVER": "https://your-custom-server.com"
      }
    }
  }
}

Deploy your own service using AntV’s GPT-Vis-SSR project.

FAQs

Q: Do I need to have AntV or Node.js installed to use this?
A: For the npx method, you only need Node.js and npm. npx downloads and runs the package without a permanent installation. For the global install method, you also just need Node.js and npm to run the npm install -g command. The server bundles all necessary AntV dependencies.

Q: What is the difference between the SSE and Streamable transports?
A: SSE (Server-Sent Events) is a web standard for pushing data from the server to the client over one connection. Streamable is another transport protocol for similar purposes. The one you choose depends on the client or platform you are integrating with. For local desktop apps, the default stdio transport is used.

Q: Can I customize the appearance of generated charts?
A: Yes, each chart generation tool accepts configuration parameters for colors, themes, labels, and other visual properties following AntV’s chart options.

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FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

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