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Maurizio Albari b594fc2dbd fix: update serve:http to run main.ts instead of server.ts (#315 ) After commit `65a62a9f`, the server startup code was moved from server.ts to main.ts, but the serve:http script was not updated accordingly. This caused 9 example servers to fail to start when running npm start: - budget-allocator-server - cohort-heatmap-server - customer-segmentation-server - integration-server - map-server - scenario-modeler-server - system-monitor-server - threejs-server - wiki-explorer-server The server.ts file now only exports createServer(), while main.ts contains the actual server startup logic.		2026-01-20 17:57:08 +00:00
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src	feat: compile example servers to JavaScript for npm consumption (#259 )	2026-01-14 23:46:17 +00:00
.gitignore	Improve consistency and organization of examples for AI learning	2026-01-13 20:48:02 -06:00
grid-cell.png	feat(basic-host): URL params, fullscreen mode, unified layout (#266 )	2026-01-14 22:09:41 +00:00
main.ts	feat: compile example servers to JavaScript for npm consumption (#259 )	2026-01-14 23:46:17 +00:00
mcp-app.html	Add color-scheme meta tag to all example apps	2025-12-15 11:42:18 -08:00
package.json	fix: update serve:http to run main.ts instead of server.ts (#315 )	2026-01-20 17:57:08 +00:00
README.md	docs: add MCP client configuration to example READMEs	2026-01-16 14:00:57 +00:00
screenshot.png	feat(basic-host): URL params, fullscreen mode, unified layout (#266 )	2026-01-14 22:09:41 +00:00
server.ts	fix(examples): resolve DIST_DIR path for npm package execution (#277 )	2026-01-15 12:23:13 +00:00
tsconfig.json	Add customer-segmentation-server example	2025-12-04 14:32:47 -06:00
tsconfig.server.json	feat: compile example servers to JavaScript for npm consumption (#259 )	2026-01-14 23:46:17 +00:00
vite.config.ts	Add customer-segmentation-server example	2025-12-04 14:32:47 -06:00

README.md

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.

MCP Client Configuration

Add to your MCP client configuration (stdio transport):

{
  "mcpServers": {
    "customer-segmentation": {
      "command": "npx",
      "args": [
        "-y",
        "--silent",
        "--registry=https://registry.npmjs.org/",
        "@modelcontextprotocol/server-customer-segmentation",
        "--stdio"
      ]
    }
  }
}

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

Install dependencies:
```
npm install
```

Build and start the server:

npm run start:http  # for Streamable HTTP transport
# OR
npm run start:stdio  # for stdio transport

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