AntV Chart MCP Server

The Antvis MCP Server Chart is a dedicated Model Context Protocol (MCP) service that empowers AI assistants to generate a wide array of data visualizations on demand. By exposing chart‑generation tools as MCP endpoints, the server eliminates the need for developers to embed complex charting libraries directly into their applications. Instead, a simple tool invocation can produce high‑quality images for area, bar, line, pie, radar, scatter, treemap, word cloud, and many other chart types supported by the AntV ecosystem.

This capability is particularly valuable for data‑centric AI workflows. When an assistant needs to explain insights, summarize trends, or illustrate comparisons, it can request a chart from the server, receive an image URL, and embed that visual directly into its response. This streamlines the creation of interactive reports, dashboards, or educational content without requiring front‑end rendering logic on the client side. The server’s TypeScript implementation ensures type safety and seamless integration with existing MCP tooling, while its lightweight API keeps latency low for real‑time conversations.

Key features of the server include:

• Extensive chart library: 15+ chart types from AntV, covering common analytical visualizations and more niche diagrams such as fishbone or network graphs.
• Image‑based output: Each tool returns a URL pointing to the rendered chart image, making it easy for assistants to embed visuals in Markdown or HTML.
• Simple MCP integration: Developers can add the server to their Claude Desktop configuration with a single JSON snippet, leveraging existing MCP tooling without custom code.
• Open source and MIT licensed: The server is free to use, modify, and distribute, encouraging community contributions and rapid iteration.

Typical use cases span data science notebooks that need quick visual feedback, business intelligence tools that integrate AI explanations with charts, and educational platforms where assistants generate illustrative diagrams on the fly. In any scenario where a conversational agent must translate raw data into an intuitive visual format, this MCP server offers a plug‑and‑play solution that keeps the focus on the assistant’s intelligence rather than on rendering mechanics.

By offloading chart generation to a dedicated MCP service, developers can build richer AI experiences that blend natural language explanations with instantly generated, publication‑ready visuals—all while maintaining a clean separation of concerns between data processing and presentation.