Data Visualization MCP Server

Name: Data Visualization MCP Server
Author: Mavline

@Mavline

Repository

About Data Visualization MCP Server

LLM-connected data visualization workflow using Vega-Lite.

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

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

FAQ from Data Visualization MCP Server

What are the runtime requirements?

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

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.

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.

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.

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.

Comments

More Data & Analytics MCP servers

Google Ads MCP

cohnen

An MCP tool that connects Google Ads with Claude AI/Cursor and others, allowing you to analyze your advertising data through natural language conversations. This integration gives you access to campaign information, performance metrics, keyword analytics, and ad management—all th

657