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Data Visualization MCP Server

by markomitranic
GitHub
Python
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Integrations

  • Provides containerized deployment of the data visualization server, making it easy to run in isolated environments.

  • Powers data visualization through Vega-Lite syntax, allowing users to create customized charts and graphs from saved data tables.

Data Visualization MCP Server

Overview

A Model Context Protocol (MCP) server implementation that provides the LLM an interface for visualizing data using Vega-Lite syntax.

Components

Tools

The server offers two core tools:

  • save_data
    • Save a table of data agregations to the server for later visualization
    • Input:
      • name (string): Name of the data table to be saved
      • data (array): Array of objects representing the data table
    • Returns: success message
  • visualize_data
    • Visualize a table of data using Vega-Lite syntax
    • Input:
      • data_name (string): Name of the data table to be visualized
      • vegalite_specification (string): JSON string representing the Vega-Lite specification
    • Returns: If the --output_type is set to text, returns a success message with an additional artifact key containing the complete Vega-Lite specification with data. If the --output_type is set to png, returns a base64 encoded PNG image of the visualization using the MPC ImageContent container.

Usage with Claude Desktop

# Add the server to your claude_desktop_config.json { "mcpServers": { "datavis": { "command": "uv", "args": [ "--directory", "/absolute/path/to/mcp-datavis-server", "run", "mcp_server_vegalite", "--output-type", "png" # or "text" ] } } }

Usage with uv

uv --directory /Users/markomitranic/Sites/mcp/mcp-vegalite-server run mcp_server_vegalite --output-type png

Usage with Docker

docker build -t mcp-server-vegalite . docker run -i --rm mcp-server-vegalite --output-type png

This server cannot be installed

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hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

An MCP server that allows AI models to create data visualizations using Vega-Lite syntax by providing tools to save data tables and generate visualizations from them.

  1. Overview
    1. Components
      1. Tools
    2. Usage with Claude Desktop
      1. Usage with uv
        1. Usage with Docker

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