analytics-data

MCP Server Chart

antvis

by antvis

Effortlessly create 25+ chart types with MCP Server Chart. Visualize complex datasets using TypeScript and AntV for powe

Generate 25+ diverse chart types effortlessly with MCP Server Chart powered by AntV. This TypeScript-based server supports area, bar, line, pie, radar, Sankey, word cloud, and many other charts, including geographic visualizations specific to China. It enables clear and interactive display of data trends, distributions, relationships, and hierarchies. Flexible tool filtering and record management features enhance customization and monitoring. Perfect for visualizing complex datasets, MCP Server Chart integrates smoothly with various MCP tools and environments, providing developers with a powerful, easy-to-use chart generation solution to communicate data visually and effectively.

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25+ chart types supportedBuilt on AntV libraryTypeScript-based server

best for

  • / Data analysts visualizing complex datasets
  • / Developers building dashboards and reports
  • / Business users presenting metrics and KPIs
  • / Researchers analyzing statistical distributions

capabilities

  • / Generate bar, column, and line charts
  • / Create pie, radar, and funnel charts
  • / Build Sankey diagrams and word clouds
  • / Generate boxplots and histograms
  • / Create geographic maps for China regions
  • / Build flow diagrams and fishbone charts

what it does

Generate 25+ chart types (bar, line, pie, radar, Sankey, word cloud, etc.) using AntV visualization library. Creates interactive charts from data for analysis and presentation.

about

MCP Server Chart is a community-built MCP server published by antvis that provides AI assistants with tools and capabilities via the Model Context Protocol. Effortlessly create 25+ chart types with MCP Server Chart. Visualize complex datasets using TypeScript and AntV for powe It is categorized under analytics data. This server exposes 26 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install MCP Server Chart in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport. This server supports remote connections over HTTP, so no local installation is required.

license

MIT

MCP Server Chart is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP Server Chart

A Model Context Protocol server for generating charts using AntV. We can use this mcp server for chart generation and data analysis.

build npm Version npm License codecov smithery installations badge Visitors

<img width="768" alt="mcp-server-chart technical digram" src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*XVH-Srg-b9UAAAAAgGAAAAgAemJ7AQ/fmt.avif" />

This is a TypeScript-based MCP server that provides chart generation capabilities. It allows you to create various types of charts through MCP tools. You can also use it in Dify.

📋 Table of Contents

✨ Features

Now 26+ charts supported.

<img width="768" alt="mcp-server-chart preview" src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*IyIRQIQHyKYAAAAAgCAAAAgAemJ7AQ/fmt.avif" />
  1. generate_area_chart: Generate an area chart, used to display the trend of data under a continuous independent variable, allowing observation of overall data trends.
  2. generate_bar_chart: Generate a bar chart, used to compare values across different categories, suitable for horizontal comparisons.
  3. generate_boxplot_chart: Generate a boxplot, used to display the distribution of data, including the median, quartiles, and outliers.
  4. generate_column_chart: Generate a column chart, used to compare values across different categories, suitable for vertical comparisons.
  5. generate_district_map - Generate a district-map, used to show administrative divisions and data distribution.
  6. generate_dual_axes_chart: Generate a dual-axes chart, used to display the relationship between two variables with different units or ranges.
  7. generate_fishbone_diagram: Generate a fishbone diagram, also known as an Ishikawa diagram, used to identify and display the root causes of a problem.
  8. generate_flow_diagram: Generate a flowchart, used to display the steps and sequence of a process.
  9. generate_funnel_chart: Generate a funnel chart, used to display data loss at different stages.
  10. generate_histogram_chart: Generate a histogram, used to display the distribution of data by dividing it into intervals and counting the number of data points in each interval.
  11. generate_line_chart: Generate a line chart, used to display the trend of data over time or another continuous variable.
  12. generate_liquid_chart: Generate a liquid chart, used to display the proportion of data, visually representing percentages in the form of water-filled spheres.
  13. generate_mind_map: Generate a mind-map, used to display thought processes and hierarchical information.
  14. generate_network_graph: Generate a network graph, used to display relationships and connections between nodes.
  15. generate_organization_chart: Generate an organizational chart, used to display the structure of an organization and personnel relationships.
  16. generate_path_map - Generate a path-map, used to display route planning results for POIs.
  17. generate_pie_chart: Generate a pie chart, used to display the proportion of data, dividing it into parts represented by sectors showing the percentage of each part.
  18. generate_pin_map - Generate a pin-map, used to show the distribution of POIs.
  19. generate_radar_chart: Generate a radar chart, used to display multi-dimensional data comprehensively, showing multiple dimensions in a radar-like format.
  20. generate_sankey_chart: Generate a sankey chart, used to display data flow and volume, representing the movement of data between different nodes in a Sankey-style format.
  21. generate_scatter_chart: Generate a scatter plot, used to display the relationship between two variables, showing data points as scattered dots on a coordinate system.
  22. generate_treemap_chart: Generate a treemap, used to display hierarchical data, showing data in rectangular forms where the size of rectangles represents the value of the data.
  23. generate_venn_chart: Generate a venn diagram, used to display relationships between sets, including intersections, unions, and differences.
  24. generate_violin_chart: Generate a violin plot, used to display the distribution of data, combining features of boxplots and density plots to provide a more detailed view of the data distribution.
  25. generate_word_cloud_chart: Generate a word-cloud, used to display the frequency of words in textual data, with font sizes indicating the frequency of each word.
  26. generate_spreadsheet: Generate a spreadsheet or pivot table for displaying tabular data. When 'rows' or 'values' fields are provided, it renders as a pivot table (cross-tabulation); otherwise, it renders as a regular table.

[!NOTE] The above geographic visualization chart generation tool uses AMap service and currently only supports map generation within China.

🤖 Usage

To use with Desktop APP, such as Claude, VSCode, Cline, Cherry Studio, Cursor, and so on, add the MCP server config below. On Mac system:

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

On Window system:

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

Also, you can use it on aliyun, modelscope, glama.ai, smithery.ai or others with HTTP, SSE Protocol.

🎨 Skill Usage

If you are using an AI IDE with skill support (like Claude Code), you can use the chart-visualization skill to automatically select the best chart type and generate visualizations.

You can add the skill from https://github.com/antvis/chart-visualization-skills using:

npx skills add antvis/chart-visualization-skills

Then provide your data or describe the visualization you want. The skill will intelligently choose from 25+ chart types and generate the chart for you.

🚰 Run with SSE or Streamable transport

Run directly

Install the package globally.

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

Run the server with your preferred transport option:

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

# For Streamable transport with custom endpoint
mcp-server-chart --transport streamable

Then you can access the server at:

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

Docker deploy

Enter the docker directory.

cd docker

Deploy using docker-compose.

docker compose up -d

Then you can access the server at:

  • SSE transport: http://localhost:1123/sse
  • Streamable transport: http://localhost:1122/mcp

🎮 CLI Options

You can also use the following CLI options when running the MCP server. Command options by run cli with -H.

MCP Server Chart CLI

Options:
  --transport, -t  Specify the transport protocol: "stdio", "sse", or "streamable" (default: "stdio")
  --host, -h       Specify the host for SSE or streamable transport (default: localhost)
  --port, -p       Specify the port for SSE or streamable transport (default: 1122)
  --endpoint, -e   Specify the endpoint for the transport:
                   - For SSE: default is "/sse"
                   - For streamable: default is "/mcp"
  --help, -H       Show this help message

⚙️ Environment Variables

VariableDescriptionDefaultExample
VIS_REQUEST_SERVERCustom chart generation service URL for private deploymenthttps://antv-studio.alipay.com/api/gpt-vishttps://your-server.com/api/chart
SERVICE_IDService identifier for chart generation records-your-service-id-123

FAQ

What is the MCP Server Chart MCP server?
MCP Server Chart is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for MCP Server Chart?
This profile displays 45 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.545 reviews
  • Kaira Mehta· Dec 28, 2024

    According to our notes, MCP Server Chart benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Ishan Mensah· Dec 24, 2024

    MCP Server Chart is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Chinedu Taylor· Dec 8, 2024

    I recommend MCP Server Chart for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Chinedu Sethi· Nov 27, 2024

    MCP Server Chart reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Kabir Diallo· Nov 19, 2024

    We wired MCP Server Chart into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Aarav Abebe· Nov 3, 2024

    We evaluated MCP Server Chart against two servers with overlapping tools; this profile had the clearer scope statement.

  • William Tandon· Oct 22, 2024

    MCP Server Chart is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Kiara Farah· Oct 18, 2024

    Useful MCP listing: MCP Server Chart is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Ishan Rahman· Oct 10, 2024

    MCP Server Chart is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Yash Thakker· Sep 17, 2024

    MCP Server Chart has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

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