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Data Visualization MCP Server

@Mavline

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

Mavline

关于 Data Visualization MCP Server

LLM-connected data visualization workflow using Vega-Lite.

#data-visualization#mcp-server#vega-lite

Data Visualization MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-server-vegalite": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Data Visualization MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Data Visualization MCP Server?

Data Visualization MCP Server is a Model Context Protocol server that enables LLM-connected workflows to store tabular data and render Vega-Lite visualizations. It provides a technical integration layer for AI-assisted reporting, data visualization, and analytics prototypes where an LLM needs to turn structured data into charts without manual tool switching.

How to use Data Visualization MCP Server?

Install using uv or another Python environment manager with Python 3.10+. Run the server with uv run mcp_server_vegalite --output-type png for PNG output or uv run mcp_server_vegalite --output-type text for text/spec artifacts. The server exposes two core tools: one to save a named table of JSON-like rows, and another to accept a Vega-Lite specification, attach the saved data, render the chart, and return the result.

Key features of Data Visualization MCP Server

  • Save named tabular datasets for later visualization.
  • Generate Vega-Lite visualizations from saved datasets.
  • Return visualization output as text/spec or PNG images.
  • Support stdio transport for local MCP clients.
  • Include an SSE/FastAPI path for integration experiments.
  • Store generated visualization files locally during runs.

Use cases of Data Visualization MCP Server

  • AI-assisted reporting: an LLM produces a chart from structured query results.
  • Data visualization workflows: turn tabular data into charts without manual tool switching.
  • Analytics prototypes: quickly generate Vega-Lite specs from language-model outputs.
  • Integration layers between LLMs and business data for dashboards or reports.
  • Portfolio/proof-of-work projects demonstrating LLM-connected data visualization.

常见问题

1

What are the runtime requirements?

Python 3.10 or newer and `uv` (or another Python environment manager) are required.

2

What transport protocols does the server support?

The server supports stdio transport for local MCP clients and includes an SSE/FastAPI path for integration experiments.

3

Can the server return images?

Yes. Run with `--output-type png` to return PNG images, or `--output-type text` to return Vega-Lite specification text.

4

What kind of data can be saved and visualized?

The server saves named tables of JSON-like rows. Vega-Lite specifications are attached to this saved data for rendering.

5

Where are generated visualization files stored?

Generated visualization files are stored locally during runs. No private client data, production credentials, or internal datasets are included.

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