Visuals MCP
V

Visuals MCP

An interactive visualization server based on the MCP protocol, providing rich visualization components for AI agents, including tables, image previews, master-detail views, tree structures, and draggable lists.
2 points
0

What is MCP Visuals Server?

MCP Visuals Server is a server based on the Model Context Protocol (MCP), specifically designed to provide rich data visualization capabilities for AI assistants (such as GitHub Copilot, Claude, etc.). It allows AI assistants to display data in the form of interactive charts, tables, images, etc., greatly enhancing the user experience when AI assistants handle complex data.

How to use MCP Visuals Server?

After installation, the AI assistant will automatically recognize available visualization tools. When you ask data-related questions, the AI assistant will use appropriate visualization components to display the results. You can directly interact with these visualization components, such as sorting tables, filtering data, expanding tree nodes, etc. These interaction states will be automatically fed back to the AI assistant to enable more intelligent conversations.

Applicable scenarios

Suitable for various scenarios that require data analysis and visualization, including: data analysis reports, file system browsing, project management, data comparison, hierarchical structure display, etc. Whether developers are analyzing log data or project managers are viewing task lists, they can obtain a better visual experience.

Main features

Table visualization
A fully functional interactive data table that supports sorting, filtering, pagination, column hiding, row selection, data export, etc. Built on TanStack Table v8, it can handle data of any structure.
Image preview
Displays images and their metadata (file name, size, dimensions, etc.), supporting URLs, data URIs, and local file paths. Automatically adapts to the VS Code theme color.
Master-detail view
Displays a list of items on the left and detailed information of the selected item on the right. Supports horizontal or vertical layout, and the details panel can display tables, images, or custom content.
Tree view
Displays hierarchical data structures, supporting node expansion/collapse, selection, and custom icons. Suitable for scenarios such as file systems, organizational structures, and classification directories.
List visualization
An interactive list that supports drag-and-drop sorting, checkboxes, image thumbnails, and compact mode switching. Suitable for scenarios such as task lists and project inventories.
Theme integration
All components automatically adapt to the VS Code theme color and font, providing a consistent visual experience.
AI assistant integration
The interaction states of users with visualization components (such as selected rows and applied filters) will be automatically sent back to the AI assistant to enable context-aware intelligent conversations.
Advantages
Rich visualization components: Provides 5 different types of visualization methods to meet diverse needs
Fully interactive: Users can directly operate data without repeatedly requesting through the AI assistant
Seamless integration: Deeply integrated with VS Code and mainstream AI assistants, ready to use after installation
Consistent theme: Automatically adapts to the system theme for a unified visual experience
State preservation: User operation states are automatically synchronized to the AI assistant for intelligent context
Data export: Supports export in multiple formats (CSV, PDF, PNG, JSON, etc.)
Limitations
Depends on the MCP protocol: Only applicable to AI assistants that support the MCP protocol
Requires installation: The server needs to be installed and configured on the client
Performance limitations: There may be performance considerations when processing extremely large data sets
Learning curve: Users need to understand the interaction methods of different visualization components

How to use

Choose an installation method
Choose an appropriate installation method based on your usage scenario: - VS Code users: Install directly from the VS Code Extension Marketplace - Other MCP clients: Install via npm - Developers: Build from source code
Configure the MCP client
Configure the MCP server in your AI assistant client. VS Code users are usually automatically configured, while other clients may need to manually add the server configuration.
Start using
Restart the AI assistant client. Now you can request data visualization functions from the AI assistant. For example, ask "Please display this data in a table" or "Show the tree structure of this folder".
Interact with visualization components
Directly click on the table column headers to sort, use filters to filter data, drag list items to reorder, expand tree nodes, etc. Your operations will be automatically fed back to the AI assistant.

Usage examples

Data analysis report
Analyze sales data and generate an interactive report
File system browsing
Browse and analyze the project file structure
Task management
Manage the project task list
Image library management
View and manage image resources
User data management
Browse and filter user data

Frequently Asked Questions

Which AI assistants does MCP Visuals Server support?
Do I need programming knowledge to use it?
How is data security ensured?
What size of data sets does it support?
Can I customize the visualization style?
What should I do if I don't see the visualization function after installation?
Which formats does it support for export?
How do I update to the latest version?

Related resources

MCP official documentation
Official API documentation and specifications for the Model Context Protocol
MCP application examples
Official MCP application example code
VS Code MCP development guide
Detailed guide for developing MCP servers in VS Code
TanStack Table documentation
Documentation for the TanStack Table framework used by the table component
GitHub repository
Source code repository for MCP Visuals Server
npm package page
Package information and installation instructions on npm

Installation

Copy the following command to your Client for configuration
{
  "github.copilot.chat.mcpServers": {
    "visuals-mcp": {
			"type": "stdio",
			"command": "node",
			"args": [
				"dist/server.js"
			]
		}
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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