npm package: @modelcontextprotocol/server-customer-segmentation
Description: Customer segmentation MCP App Server with filtering
Latest version: 1.5.0

# 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.

<table>
  <tr>
    <td><a href="https://apps.extensions.modelcontextprotocol.io/screenshots/customer-segmentation-server/01-account-age-revenue.png"><img src="https://apps.extensions.modelcontextprotocol.io/screenshots/customer-segmentation-server/01-account-age-revenue.png" alt="Account age vs revenue" width="100%"></a></td>
    <td><a href="https://apps.extensions.modelcontextprotocol.io/screenshots/customer-segmentation-server/02-tickets-nps-engagement.png"><img src="https://apps.extensions.modelcontextprotocol.io/screenshots/customer-segmentation-server/02-tickets-nps-engagement.png" alt="Tickets, NPS, engagement" width="100%"></a></td>
    <td><a href="https://apps.extensions.modelcontextprotocol.io/screenshots/customer-segmentation-server/07-age-engagement-revenue.png"><img src="https://apps.extensions.modelcontextprotocol.io/screenshots/customer-segmentation-server/07-age-engagement-revenue.png" alt="Age, engagement, revenue" width="100%"></a></td>
  </tr>
</table>

## MCP Client Configuration

Add to your MCP client configuration (stdio transport):

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

### Local Development

To test local modifications, use this configuration (replace `~/code/ext-apps` with your clone path):

```json
{
  "mcpServers": {
    "customer-segmentation": {
      "command": "bash",
      "args": [
        "-c",
        "cd ~/code/ext-apps/examples/customer-segmentation-server && npm run build >&2 && node dist/index.js --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

1. Install dependencies:

   ```bash
   npm install
   ```

2. Build and start the server:

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

3. View using the [`basic-host`](https://github.com/modelcontextprotocol/ext-apps/tree/main/examples/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