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Example: Customer Segmentation Explorer

A demo MCP App that displays customer data as an interactive scatter/bubble chart with segment-based clustering. Users can explore different metrics, filter by segment, and click to see detailed customer information.

Account age vs revenue Tickets, NPS, engagement Age, engagement, revenue

Features

  • Interactive Scatter Plot: Bubble chart visualization using Chart.js with configurable X/Y axes
  • Segment Clustering: 250 customers grouped into 4 segments (Enterprise, Mid-Market, SMB, Startup)
  • Axis Selection: Choose from 6 metrics for each axis (Revenue, Employees, Account Age, Engagement, Tickets, NPS)
  • Size Mapping: Optional bubble sizing by a third metric for additional data dimension
  • Legend Filtering: Click segment pills to show/hide customer groups
  • Detail Panel: Hover or click customers to see name, segment, revenue, engagement, and NPS
  • Theme Support: Adapts to light/dark mode preferences

Running

  1. Install dependencies:

    npm install
    
  2. Build and start the server:

    npm run start:http  # for Streamable HTTP transport
    # OR
    npm run start:stdio  # for stdio transport
    
  3. View using the basic-host example or another MCP Apps-compatible host.

Architecture

Server (server.ts)

Exposes a single get-customer-data tool that returns:

  • Array of 250 generated customer records with segment assignments
  • Segment summary with counts and colors for each group
  • Optional segment filter parameter

The tool is linked to a UI resource via _meta.ui.resourceUri.

App (src/mcp-app.ts)

  • Uses Chart.js bubble chart for the visualization
  • Fetches data once on connection
  • Dropdown controls update chart axes and bubble sizing
  • Custom legend with clickable segment toggles
  • Detail panel updates on hover/click interactions

Data Generator (src/data-generator.ts)

  • Generates realistic customer data with Gaussian clustering around segment centers
  • Each segment has characteristic ranges for revenue, employees, engagement, etc.
  • Company names generated from word-list combinations (e.g., "Apex Data Corp")
  • Data cached in memory for consistency across requests