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Add App To Server

OrganizationPopular
tldraw
add-app-to-server

This skill should be used when the user asks to "add an app to my MCP server", "add UI to my MCP server", "add a view to my MCP tool", "enrich MCP tools with UI", "add interactive UI to existing server", "add MCP Apps to my server", or needs to add interactive UI capabilities to an existing MCP server that already has tools. Provides guidance for analyzing existing tools and adding MCP Apps UI resources.

Overview

Publishertldraw
Repositorytldraw
Skill nameadd-app-to-server
Stars
50.8K
Forks
3.6K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tldraw on GitHub. Read the source before you install it.

Installation

Install the Add App To Server AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/tldraw/tldraw/tree/main/apps/mcp-app/.claude/skills/add-app-to-server
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tldraw/tldraw.git /tmp/tldraw
mkdir -p .claude/skills
cp -r /tmp/tldraw/apps/mcp-app/.claude/skills/add-app-to-server .claude/skills/add-app-to-server
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add App To Server in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Add App To Server on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Add App To Server is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Add UI to MCP Server

Enrich an existing MCP server's tools with interactive UIs using the MCP Apps SDK (@modelcontextprotocol/ext-apps).

How It Works

Existing tools get paired with HTML resources that render inline in the host's conversation. The tool continues to work for text-only clients — UI is an enhancement, not a replacement. Each tool that benefits from UI gets linked to a resource via _meta.ui.resourceUri, and the host renders that resource in a sandboxed iframe when the tool is called.

Getting Reference Code

Clone the SDK repository for working examples and API documentation:

bash
git clone --branch "v$(npm view @modelcontextprotocol/ext-apps version)" --depth 1 https://github.com/modelcontextprotocol/ext-apps.git /tmp/mcp-ext-apps

API Reference (Source Files)

Read JSDoc documentation directly from /tmp/mcp-ext-apps/src/:

FileContents
src/app.tsApp class, handlers (ontoolinput, ontoolresult, onhostcontextchanged, onteardown), lifecycle
src/server/index.tsregisterAppTool, registerAppResource, getUiCapability, tool visibility options
src/spec.types.tsAll type definitions: McpUiHostContext, CSS variable keys, display modes
src/styles.tsapplyDocumentTheme, applyHostStyleVariables, applyHostFonts
src/react/useApp.tsxuseApp hook for React apps
src/react/useHostStyles.tsuseHostStyles, useHostStyleVariables, useHostFonts hooks

Key Examples (Mixed Tool Patterns)

These examples demonstrate servers with both App-enhanced and plain tools — the exact pattern you're adding:

ExamplePattern
examples/map-server/show-map (App tool) + geocode (plain tool)
examples/pdf-server/display_pdf (App tool) + list_pdfs (plain tool) + read_pdf_bytes (app-only tool)
examples/system-monitor-server/get-system-info (App tool) + poll-system-stats (app-only polling tool)

Framework Templates

Learn and adapt from /tmp/mcp-ext-apps/examples/basic-server-{framework}/:

TemplateKey Files
basic-server-vanillajs/server.ts, src/mcp-app.ts, mcp-app.html
basic-server-react/server.ts, src/mcp-app.tsx (uses useApp hook)
basic-server-vue/server.ts, src/App.vue
basic-server-svelte/server.ts, src/App.svelte
basic-server-preact/server.ts, src/mcp-app.tsx
basic-server-solid/server.ts, src/mcp-app.tsx

Step 1: Analyze Existing Tools

Before writing any code, analyze the server's existing tools and determine which ones benefit from UI.

  1. Read the server source and list all registered tools
  2. For each tool, assess whether it would benefit from UI (returns data that could be visualized, involves user interaction, etc.) vs. is fine as text-only (simple lookups, utility functions)
  3. Identify tools that could become app-only helpers (data the UI needs to poll/fetch but the model doesn't need to call directly)
  4. Present the analysis to the user and confirm which tools to enhance

Decision Framework

Tool output typeUI benefitExample
Structured data / lists / tablesHigh — interactive table, search, filteringList of items, search results
Metrics / numbers over timeHigh — charts, gauges, dashboardsSystem stats, analytics
Media / rich contentHigh — viewer, player, rendererMaps, PDFs, images, video
Simple text / confirmationsLow — text is fine"File created", "Setting updated"
Data for other toolsConsider app-onlyPolling endpoints, chunk loaders

Step 2: Add Dependencies

bash
npm install @modelcontextprotocol/ext-apps
npm install -D vite vite-plugin-singlefile

Plus framework-specific dependencies if needed (e.g., react, react-dom, @vitejs/plugin-react for React).

Use npm install to add dependencies rather than manually writing version numbers. This lets npm resolve the latest compatible versions. Never specify version numbers from memory.

Step 3: Set Up the Build Pipeline

Vite Configuration

Create vite.config.ts with vite-plugin-singlefile to bundle the UI into a single HTML file:

typescript
import { defineConfig } from 'vite'
import { viteSingleFile } from 'vite-plugin-singlefile'

export default defineConfig({
	plugins: [viteSingleFile()],
	build: {
		outDir: 'dist',
		rollupOptions: {
			input: 'mcp-app.html', // one per UI, or one shared entry
		},
	},
})

HTML Entry Point

Create mcp-app.html (or one per distinct UI if tools need different views):

html
<!doctype html>
<html lang="en">
	<head>
		<meta charset="UTF-8" />
		<meta name="viewport" content="width=device-width, initial-scale=1.0" />
		<title>MCP App</title>
	</head>
	<body>
		<div id="root"></div>
		<script type="module" src="./src/mcp-app.ts"></script>
	</body>
</html>

Build Scripts

Add build scripts to package.json. The UI must be built before the server code bundles it:

json
{
	"scripts": {
		"build:ui": "vite build",
		"build:server": "tsc",
		"build": "npm run build:ui && npm run build:server",
		"serve": "tsx server.ts"
	}
}

Step 4: Convert Tools to App Tools

Transform plain MCP tools into App tools with UI.

Before (plain MCP tool):

typescript
server.tool('my-tool', { param: z.string() }, async (args) => {
	const data = await fetchData(args.param)
	return { content: [{ type: 'text', text: JSON.stringify(data) }] }
})

After (App tool with UI):

typescript
import {
	registerAppTool,
	registerAppResource,
	RESOURCE_MIME_TYPE,
} from '@modelcontextprotocol/ext-apps/server'

const resourceUri = 'ui://my-tool/mcp-app.html'

registerAppTool(
	server,
	'my-tool',
	{
		description: 'Shows data with an interactive UI',
		inputSchema: { param: z.string() },
		_meta: { ui: { resourceUri } },
	},
	async (args) => {
		const data = await fetchData(args.param)
		return {
			content: [{ type: 'text', text: JSON.stringify(data) }], // text fallback for non-UI hosts
			structuredContent: { data }, // structured data for the UI
		}
	}
)

Key guidance:

  • Always keep the content array with a text fallback for text-only clients
  • Add structuredContent for data the UI needs to render
  • Link the tool to its resource via _meta.ui.resourceUri
  • Leave tools that don't benefit from UI unchanged — they stay as plain tools

Step 5: Register Resources

Register the HTML resource so the host can fetch it:

typescript
import fs from 'node:fs/promises'
import path from 'node:path'

const resourceUri = 'ui://my-tool/mcp-app.html'

registerAppResource(
	server,
	{
		uri: resourceUri,
		name: 'My Tool UI',
		mimeType: RESOURCE_MIME_TYPE,
	},
	async () => {
		const html = await fs.readFile(
			path.resolve(import.meta.dirname, 'dist', 'mcp-app.html'),
			'utf-8'
		)
		return { contents: [{ uri: resourceUri, mimeType: RESOURCE_MIME_TYPE, text: html }] }
	}
)

If multiple tools share the same UI, they can reference the same resourceUri and the same resource registration.

Step 6: Build the UI

Handler Registration

Register ALL handlers BEFORE calling app.connect():

typescript
import {
	App,
	PostMessageTransport,
	applyDocumentTheme,
	applyHostStyleVariables,
	applyHostFonts,
} from '@modelcontextprotocol/ext-apps'

const app = new App({ name: 'My App', version: '1.0.0' })

app.ontoolinput = (params) => {
	// Render the UI using params.arguments and/or params.structuredContent
}

app.ontoolresult = (result) => {
	// Update UI with final tool result
}

app.onhostcontextchanged = (ctx) => {
	if (ctx.theme) applyDocumentTheme(ctx.theme)
	if (ctx.styles?.variables) applyHostStyleVariables(ctx.styles.variables)
	if (ctx.styles?.css?.fonts) applyHostFonts(ctx.styles.css.fonts)
	if (ctx.safeAreaInsets) {
		const { top, right, bottom, left } = ctx.safeAreaInsets
		document.body.style.padding = `${top}px ${right}px ${bottom}px ${left}px`
	}
}

app.onteardown = async () => {
	return {}
}

await app.connect(new PostMessageTransport())

Host Styling

Use host CSS variables for theme integration:

css
.container {
	background: var(--color-background-secondary);
	color: var(--color-text-primary);
	font-family: var(--font-sans);
	border-radius: var(--border-radius-md);
}

Key variable groups: --color-background-*, --color-text-*, --color-border-*, --font-sans, --font-mono, --font-text-*-size, --font-heading-*-size, --border-radius-*. See src/spec.types.ts for the full list.

For React apps, use the useApp and useHostStyles hooks instead — see basic-server-react/ for the pattern.

Optional Enhancements

App-Only Helper Tools

Tools the UI calls but the model doesn't need to invoke directly (polling, pagination, chunk loading):

typescript
registerAppTool(
	server,
	'poll-data',
	{
		description: 'Polls latest data for the UI',
		_meta: { ui: { resourceUri, visibility: ['app'] } },
	},
	async () => {
		const data = await getLatestData()
		return { content: [{ type: 'text', text: JSON.stringify(data) }] }
	}
)

The UI calls these via app.callServerTool("poll-data", {}).

CSP Configuration

If the UI needs to load external resources (fonts, APIs, CDNs), declare the domains:

typescript
registerAppResource(
	server,
	{
		uri: resourceUri,
		name: 'My Tool UI',
		mimeType: RESOURCE_MIME_TYPE,
		_meta: {
			ui: {
				connectDomains: ['api.example.com'], // fetch/XHR targets
				resourceDomains: ['cdn.example.com'], // scripts, styles, images
				frameDomains: ['embed.example.com'], // nested iframes
			},
		},
	},
	async () => {
		/* ... */
	}
)

Streaming Partial Input

For large tool inputs, show progress during LLM generation:

typescript
app.ontoolinputpartial = (params) => {
	const args = params.arguments // Healed partial JSON - always valid
	// Render preview with partial data
}

app.ontoolinput = (params) => {
	// Final complete input - switch to full render
}

Graceful Degradation with getUiCapability()

Conditionally register App tools only when the client supports UI, falling back to text-only tools:

typescript
import {
	getUiCapability,
	registerAppTool,
	RESOURCE_MIME_TYPE,
} from '@modelcontextprotocol/ext-apps/server'

server.server.oninitialized = () => {
	const clientCapabilities = server.server.getClientCapabilities()
	const uiCap = getUiCapability(clientCapabilities)

	if (uiCap?.mimeTypes?.includes(RESOURCE_MIME_TYPE)) {
		// Client supports UI — register App tool
		registerAppTool(
			server,
			'my-tool',
			{
				description: 'Shows data with interactive UI',
				_meta: { ui: { resourceUri } },
			},
			appToolHandler
		)
	} else {
		// Text-only client — register plain tool
		server.tool('my-tool', 'Shows data', { param: z.string() }, plainToolHandler)
	}
}

Fullscreen Mode

Allow the UI to expand to fullscreen:

typescript
app.onhostcontextchanged = (ctx) => {
	if (ctx.availableDisplayModes?.includes('fullscreen')) {
		fullscreenBtn.style.display = 'block'
	}
	if (ctx.displayMode) {
		container.classList.toggle('fullscreen', ctx.displayMode === 'fullscreen')
	}
}

async function toggleFullscreen() {
	const newMode = currentMode === 'fullscreen' ? 'inline' : 'fullscreen'
	const result = await app.requestDisplayMode({ mode: newMode })
	currentMode = result.mode
}

Common Mistakes to Avoid

  1. Forgetting text content fallback — Always include content array with text for non-UI hosts
  2. Registering handlers after connect() — Register ALL handlers BEFORE calling app.connect()
  3. Missing vite-plugin-singlefile — Without it, assets won't load in the sandboxed iframe
  4. Forgetting resource registration — The tool references a resourceUri that must have a matching resource
  5. Hardcoding styles — Use host CSS variables (var(--color-*)) for theme integration
  6. Not handling safe area insets — Always apply ctx.safeAreaInsets in onhostcontextchanged

Testing

Using basic-host

Test the enhanced server with the basic-host example:

bash
# Terminal 1: Build and run your server
npm run build && npm run serve

# Terminal 2: Run basic-host (from cloned repo)
cd /tmp/mcp-ext-apps/examples/basic-host
npm install
SERVERS='["http://localhost:3001/mcp"]' npm run start
# Open http://localhost:8080

Configure SERVERS with a JSON array of your server URLs (default: http://localhost:3001/mcp).

Verify

  1. Plain tools still work and return text output
  2. App tools render their UI in the iframe
  3. ontoolinput handler fires with tool arguments
  4. ontoolresult handler fires with tool result
  5. Host styling (theme, fonts, colors) applies correctly

Frequently asked questions

What does the Add App To Server AI skill do?

This skill should be used when the user asks to "add an app to my MCP server", "add UI to my MCP server", "add a view to my MCP tool", "enrich MCP tools with UI", "add interactive UI to existing server", "add MCP Apps to my server", or needs to add interactive UI capabilities to an existing MCP server that already has tools. Provides guidance for analyzing existing tools and adding MCP Apps UI resources.

Why use Add App To Server on TypingMind?

Because you install it once and use it with any model. Add App To Server is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Add App To Server in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tldraw/tldraw/tree/main/apps/mcp-app/.claude/skills/add-app-to-server. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Add App To Server?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Add App To Server?

As many as you like. As long as a model supports skills, you can use Add App To Server with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Add App To Server AI skill free?

It is published on GitHub by tldraw. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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