AntV MCP Server Integration

What does “AntV” MCP Server do?

The AntV MCP Server is a visualization Model Context Protocol (MCP) server designed to generate over 25 types of visual charts using the AntV charting library. Acting as a bridge between AI assistants and the powerful charting capabilities of AntV, this MCP server enables AI-driven workflows to create, manipulate, and serve chart visualizations on demand. By exposing chart generation as an MCP tool, it enhances development workflows in applications that require dynamic data visualization, insight presentation, and report generation. The server empowers clients to easily request charts for analytics, dashboards, and business intelligence scenarios, all standardized via the MCP interface.

List of Prompts

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List of Resources

No explicit resources documented in the repository.

List of Tools

• Chart Generation Tool
  Enables AI clients to generate a wide range of visual charts (over 20 types, e.g., bar, line, pie, etc.) programmatically using the AntV charting system. This tool abstracts the complexity of chart creation, allowing users to specify chart type and data to receive a rendered visual output.

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Use Cases of this MCP Server

• Automated Data Visualization
  Developers can automate the creation of charts directly from data pipelines, enabling fast and standardized visual reporting in dashboards or analytics applications.
• Business Intelligence Integration
  Integrate chart generation into BI tools, allowing users and AI systems to visualize trends, comparisons, and metrics on demand.
• Report Generation
  Embed dynamic charts in automatically generated reports, improving the clarity and impact of insights presented to stakeholders.
• Educational Platforms
  Enable learning platforms to create interactive visual aids and data-driven illustrations to aid comprehension.
• AI Assistant Visualization Support
  Equip AI assistants with the ability to generate and display charts in response to user queries, making conversations more informative and visually engaging.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Open your Windsurf configuration file.
3. Locate the mcpServers section.
4. Add the AntV MCP Server using the following JSON snippet:

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

5. Save the configuration and restart Windsurf.
6. Verify the server is running from the Windsurf dashboard.

Claude

1. Install Node.js if not present.
2. Access Claude’s configuration file.
3. Add the AntV MCP Server under the mcpServers key:

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

4. Save and restart Claude.
5. Confirm the server is connected.

Cursor

1. Make sure Node.js is installed.
2. Open Cursor’s settings file.
3. Insert the AntV MCP Server configuration:

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

4. Save and restart Cursor.
5. Check the availability of chart tools in the tool palette.

Cline

1. Install Node.js if necessary.
2. Find the configuration file for Cline.
3. Add the AntV MCP Server configuration:

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

4. Save the file and restart Cline.
5. Validate the setup by requesting a chart generation.

Securing API Keys To secure API keys and sensitive inputs, use environment variables. Example configuration:

{
  "mcpServers": {
    "antv-chart": {
      "command": "npx",
      "args": ["@antv/mcp-server-chart@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "apiKey": "${API_KEY}"
      }
    }
  }
}

Replace "API_KEY" with your actual environment variable name.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "antv-chart": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “antv-chart” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The AntV MCP Server is highly specialized for chart generation and visualization, offering clear utility for analytics and visual-centric workflows. However, documentation lacks detail on prompt templates, resources, sampling, and roots support. For visualization-centric developers, it is a focused and valuable tool, but broader MCP features are not fully surfaced.

MCP Score