VegaLite MCP Server

What does “VegaLite” MCP Server do?

The VegaLite MCP Server is a Model Context Protocol (MCP) server implementation that provides large language models (LLMs) with an interface for visualizing data using Vega-Lite syntax. By connecting to this server, AI assistants and applications can offload tasks such as saving tabular data and generating visualizations (charts, graphs, etc.) defined in the Vega-Lite specification. This enhances developer workflows by enabling seamless programmatic data visualization, allowing LLMs to both manage datasets and produce custom visual outputs, which are essential for data analysis, reporting, and research. The server supports returning either the full Vega-Lite specification with data attached (in text mode) or a base64-encoded PNG image of the visualization (in image mode), making it flexible for various integration scenarios.

List of Prompts

No prompt templates are listed in the repository.

List of Resources

No explicit MCP resources are documented in the repository.

List of Tools

• save_data
  • Saves a table of data aggregations to the server for later visualization.
  • Inputs:
    • name (string): Name of the data table to be saved.
    • data (array): Array of objects representing the data table.
  • Returns: Success message.
• visualize_data
  • Visualizes a table of data using Vega-Lite syntax.
  • Inputs:
    • data_name (string): Name of the data table to be visualized.
    • vegalite_specification (string): JSON string representing the Vega-Lite specification.
  • Returns: If --output_type is set to text, returns the full Vega-Lite spec with data; if set to png, returns a base64-encoded PNG image.

Use Cases of this MCP Server

• Data Analysis and Visualization
  • Developers and data scientists can upload datasets and generate custom visualizations (e.g., bar charts, scatter plots) programmatically using Vega-Lite specs.
• Automated Reporting
  • LLMs can generate and visualize reports automatically by saving data and producing charts for business intelligence or research purposes.
• Interactive Data Exploration
  • Enables iterative exploration by saving new data tables and visualizing them on demand, streamlining the workflow for data-driven projects.
• Educational Tools
  • Can be integrated into educational platforms to allow students or users to visualize datasets and learn about data visualization principles interactively.

How to set it up

Windsurf

No setup instructions for Windsurf are listed in the repository.

Claude

1. Open your claude_desktop_config.json.
2. Locate the mcpServers object.
3. Add the VegaLite MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "datavis": {
         "command": "uv",
         "args": [
           "--directory",
           "/absolute/path/to/mcp-datavis-server",
           "run",
           "mcp_server_datavis",
           "--output_type",
           "png" // or "text"
         ]
       }
     }
   }
   

4. Save the configuration file.
5. Restart Claude Desktop and verify the server is running.

Securing API Keys

No specific instructions or examples for securing API keys are provided in the repository.

Cursor

No setup instructions for Cursor are listed in the repository.

Cline

No setup instructions for Cline are listed in the repository.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "MCP-name": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-name” to whatever the actual name of your MCP server is (e.g., “vegalite”, “data-vis”, etc.) and replace the URL with your own MCP server URL.

Overview

Based on the above tables, the VegaLite MCP Server is focused and well-documented in terms of tools and overview, but lacks information on prompts, resources, and security setup, limiting its out-of-the-box integration score.

Our opinion

The MCP VegaLite server is straightforward, with a clear interface for data visualization via LLMs. However, the absence of prompt templates, resources, and security guidance lowers its usability for more advanced or production scenarios. Its main value lies in its functional tools for saving and visualizing data, but overall completeness and extensibility are limited.

Rating: 5/10

MCP Score