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The Windows WSL job is the critical path of every CI run (589 to 752 s). Its checkout sits on the Windows drive, which WSL reaches over 9P, and writing node_modules there is what makes npm ci take about three minutes where the Linux job takes 23 s. The step now copies the tree onto the distro's ext4 filesystem before installing and building, as Microsoft's WSL guidance recommends for I/O-heavy work. The e2e job downloaded the full Chromium build (about 165 MB) on every run, but a headless run with no channel launches chromium-headless-shell and never opens it. `--only-shell` skips that download. Every tsconfig now lists the @types packages it uses (node, bun, vite/client, dom-speech-recognition) instead of relying on TypeScript auto-including everything under node_modules/@types. On TypeScript 5.9 this is a no-op: identical diagnostics across all 48 projects and byte-identical emitted .d.ts files. It is a prerequisite for TypeScript 7, which drops the auto-inclusion; with these lists the Go compiler passes all 48 projects, so that upgrade becomes a version bump once TypeDoc supports it. Two cache-based approaches were evaluated and rejected: an npm cache for the WSL job (npm ci is slow there because of 9P, not downloads, and a prefix-restored cache grows without bound) and a Playwright browser cache (Playwright's docs advise against it; the download is under 10 s). |
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| main.ts | ||
| mcp-app.html | ||
| package.json | ||
| README.md | ||
| screenshot.png | ||
| server.ts | ||
| tsconfig.json | ||
| tsconfig.server.json | ||
| vite.config.ts | ||
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.
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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"
]
}
}
}
Local Development
To test local modifications, use this configuration (replace ~/code/ext-apps with your clone path):
{
"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
-
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-hostexample 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