build-mcp-app

anthropics/claude-plugins-official · updated Apr 8, 2026

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$npx skills add https://github.com/anthropics/claude-plugins-official --skill build-mcp-app
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summary

An MCP app is a standard MCP server that also serves UI resources — interactive components rendered inline in the chat surface. Build once, runs in Claude and ChatGPT and any other host that implements the apps surface.

skill.md

Build an MCP App (Interactive UI Widgets)

An MCP app is a standard MCP server that also serves UI resources — interactive components rendered inline in the chat surface. Build once, runs in Claude and ChatGPT and any other host that implements the apps surface.

The UI layer is additive. Under the hood it's still tools, resources, and the same wire protocol. If you haven't built a plain MCP server before, the build-mcp-server skill covers the base layer. This skill adds widgets on top.


When a widget beats plain text

Don't add UI for its own sake — most tools are fine returning text or JSON. Add a widget when one of these is true:

Signal Widget type
Tool needs structured input Claude can't reliably infer Form
User must pick from a list Claude can't rank (files, contacts, records) Picker / table
Destructive or billable action needs explicit confirmation Confirm dialog
Output is spatial or visual (charts, maps, diffs, previews) Display widget
Long-running job the user wants to watch Progress / live status

If none apply, skip the widget. Text is faster to build and faster for the user.


Widgets vs Elicitation — route correctly

Before building a widget, check if elicitation covers it. Elicitation is spec-native, zero UI code, works in any compliant host.

Need Elicitation Widget
Confirm yes/no overkill
Pick from short enum overkill
Fill a flat form (name, email, date) overkill
Pick from a large/searchable list ❌ (no scroll/search)
Visual preview before choosing
Chart / map / diff view
Live-updating progress

If elicitation covers it, use it. See ../build-mcp-server/references/elicitation.md.


Architecture: two deployment shapes

Remote MCP app (most common)

Hosted streamable-HTTP server. Widget templates are served as resources; tool results reference them. The host fetches the resource, renders it in an iframe sandbox, and brokers messages between the widget and Claude.

┌──────────┐  tools/call   ┌────────────┐
│  Claude  │─────────────> │ MCP server │
│   host   │<── result ────│  (remote)  │
│          │  + widget ref │            │
│          │               │            │
│          │ resources/read│            │
│          │─────────────> │  widget    │
│ ┌──────┐ │<── template ──│  HTML/JS   │
│ │iframe│ │               └────────────┘
│ │widget│ │
│ └──────┘ │
└──────────┘

MCPB-packaged MCP app (local + UI)

Same widget mechanism, but the server runs locally inside an MCPB bundle. Use this when the widget needs to drive a local application — e.g., a file picker that browses the actual local disk, a dialog that controls a desktop app.

For MCPB packaging mechanics, defer to the build-mcpb skill. Everything below applies to both shapes.


How widgets attach to tools

A widget-enabled tool has two separate registrations:

  1. The tool declares a UI resource via _meta.ui.resourceUri. Its handler returns plain text/JSON — NOT the HTML.
  2. The resource is registered separately and serves the HTML.

When Claude calls the tool, the host sees _meta.ui.resourceUri, fetches that resource, renders it in an iframe, and pipes the tool's return value into the iframe via the ontoolresult event.

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { registerAppTool, registerAppResource, RESOURCE_MIME_TYPE }
  from "@modelcontextprotocol/ext-apps/server";
import { z } from "zod";

const server = new McpServer({ name: "contacts", version: "1.0.0" });

// 1. The tool — returns DATA, declares which UI to show
registerAppTool(server, "pick_contact", {
  description: "Open an interactive contact picker",
  inputSchema: { filter: z.string().optional() },
  _meta: { ui: { resourceUri: "ui://widgets/contact-picker.html" } },
}, async ({ filter }) => {
  const contacts = await db.contacts.search(filter);
  // Plain JSON — the widget receives this via ontoolresult
  return { content: [{ type: "text", text: JSON.stringify(contacts) }] };
});

// 2. The resource — serves the HTML
registerAppResource(
  server,
  "Contact Picker",
  "ui://widgets/contact-picker.html",
  {},
  async () => ({
    contents: [{
      uri: "ui://widgets/contact-picker.html",
      mimeType: RESOURCE_MIME_TYPE,
      text: pickerHtml,  // your HTML string
    }],
  }),
);

The URI scheme ui:// is convention. The mime type MUST be RESOURCE_MIME_TYPE ("text/html;profile=mcp-app") — this is how the host knows to render it as an interactive iframe, not just display the source.


Widget runtime — the App class

Inside the iframe, your script talks to the host via the App class from @modelcontextprotocol/ext-apps. This is a persistent bidirectional connection — the widget stays alive as long as the conversation is active, receiving new tool results and sending user actions.

<script type="module">
  /* ext-apps bundle inlined at build time → globalThis.ExtApps */
  /*__EXT_APPS_BUNDLE__*/
  const { App } = globalThis.ExtApps;

  const app = new App({ name: "ContactPicker", version: "1.0.0" }, {});

  // Set handlers BEFORE connecting
  app.ontoolresult = ({ content }) => {
    const contacts = JSON.parse(content[0].text);
    render(contacts);
  };

  await app.connect();

  // Later, when the user clicks something:
  function onPick(contact) {
    app.sendMessage({
      role: "user",
      content: [{ type: "text", text: `Selected contact: ${contact.id}` }],
    });
  }
</script>

The /*__EXT_APPS_BUNDLE__*/ placeholder gets replaced by the server at startup with the contents of @modelcontextprotocol/ext-apps/app-with-deps — see references/iframe-sandbox.md for why this is necessary and the rewrite snippet. Do not import { App } from "https://esm.sh/..."; the iframe's CSP blocks the transitive dependency fetches and the widget renders blank.

Method Direction Use for
app.ontoolresult = fn Host → widget Receive the tool's return value
app.ontoolinput = fn Host → widget Receive the tool's input args (what Claude passed)
app.sendMessage({...}) Widget → host Inject a message into the conversation
app.updateModelContext({...}) Widget → host Update context silently (no visible message)
app.callServerTool({name, arguments}) Widget → server Call another tool on your server
app.openLink({url}) Widget → host Open a URL in a new tab (sandbox blocks window.open)
app.getHostContext() / app.onhostcontextchanged Host → widget Theme (light/dark), locale, etc.

sendMessage is the typical "user picked something, tell Claude" path. updateModelContext is for state that Claude should know about but shouldn't clutter the chat. openLink is required for any outbound navigation — window.open and <a target="_blank"> are blocked by the sandbox attribute.

What widgets cannot do:

  • Access the host page's DOM, cookies, or storage
  • Make network calls to arbitrary origins (CSP-restricted — route through callServerTool)
  • Open popups or navigate directly — use app.openLink({url})
  • Load remote images reliably — inline as data: URLs server-side

Keep widgets small and single-purpose. A picker picks. A chart displays. Don't build a whole sub-app inside the iframe — split it into multiple tools with focused widgets.


Scaffold: minimal picker widget

Install:

npm install @modelcontextprotocol/sdk @modelcontextprotocol/ext-apps zod express

Server (src/server.ts):

how to use build-mcp-app

How to use build-mcp-app on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add build-mcp-app
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/claude-plugins-official --skill build-mcp-app

The skills CLI fetches build-mcp-app from GitHub repository anthropics/claude-plugins-official and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/build-mcp-app

Reload or restart Cursor to activate build-mcp-app. Access the skill through slash commands (e.g., /build-mcp-app) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.649 reviews
  • Hana Martin· Dec 28, 2024

    We added build-mcp-app from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dhruvi Jain· Dec 24, 2024

    build-mcp-app has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Mia Chawla· Dec 16, 2024

    Useful defaults in build-mcp-app — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Tariq Anderson· Dec 12, 2024

    Solid pick for teams standardizing on skills: build-mcp-app is focused, and the summary matches what you get after install.

  • Anaya Malhotra· Nov 23, 2024

    build-mcp-app is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hana Taylor· Nov 23, 2024

    build-mcp-app fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Charlotte Khanna· Nov 19, 2024

    Useful defaults in build-mcp-app — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Oshnikdeep· Nov 15, 2024

    Solid pick for teams standardizing on skills: build-mcp-app is focused, and the summary matches what you get after install.

  • Tariq Kim· Nov 7, 2024

    We added build-mcp-app from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Tariq Huang· Nov 3, 2024

    build-mcp-app has been reliable in day-to-day use. Documentation quality is above average for community skills.

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