ext-apps/examples/customer-segmentation-server
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Jonathan Hefner 9e9f5f0210
Clean up examples (#182)
* Centralize example server transport logic in shared utils

Move stdio/HTTP transport handling into `server-utils.ts` so example
servers only need to call `startServer(createServer)`. Switch HTTP
transport to stateless mode and remove legacy SSE endpoints.

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Make example servers self-contained with local server-utils

Copy `server-utils.ts` into each example's `src/` directory so examples
can be used standalone without the shared directory. Updates imports
from `"../shared/server-utils.js"` to `"./src/server-utils.js"`.

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Add `integration-server` example for E2E testing

Add dedicated `integration-server` example (duplicated from
`basic-server-react`) to preserve E2E testing capabilities while
allowing `basic-server-react` to be simplified as a minimal example.

Changes:
- Add `examples/integration-server` exercising SDK communication APIs
- Update E2E tests to use `integration-server` instead of
  `basic-server-react` and `basic-server-vanillajs`
- Use server name labels instead of indices for more robust test
  selection
- Remove `basic-react.png` and `basic-vanillajs.png` golden snapshots

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Co-Authored-By: Claude <noreply@anthropic.com>

* Simplify `basic-server-*` examples

- Clarify comments
- Return plain text from `get-time` tool instead of JSON
- Remove simulated cleanup delay from `onteardown`

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-19 11:44:27 +00:00
..
src Clean up examples (#182) 2025-12-19 11:44:27 +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 build errors in examples (#180) 2025-12-18 11:17:23 +00:00
README.md Clean up examples (#182) 2025-12-19 11:44:27 +00:00
server.ts Clean up examples (#182) 2025-12-19 11:44:27 +00:00
tsconfig.json Add customer-segmentation-server example 2025-12-04 14:32:47 -06:00
vite.config.ts Add customer-segmentation-server example 2025-12-04 14:32:47 -06:00

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