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Build Mcp App

OrganizationPopular
anthropics
build-mcp-app

This skill should be used when the user wants to build an "MCP app", add "interactive UI" or "widgets" to an MCP server, "render components in chat", build "MCP UI resources", make a tool that shows a "form", "picker", "dashboard" or "confirmation dialog" inline in the conversation, or mentions "apps SDK" in the context of MCP. Use AFTER the build-mcp-server skill has settled the deployment model, or when the user already knows they want UI widgets.

Overview

Publisheranthropics
Repositoryclaude-plugins-official
Skill namebuild-mcp-app
Stars
36.4K
Forks
4.1K
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Build Mcp App 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.
  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/anthropics/claude-plugins-official.git /tmp/claude-plugins-official
mkdir -p .claude/skills
cp -r /tmp/claude-plugins-official/plugins/mcp-server-dev/skills/build-mcp-app .claude/skills/build-mcp-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build Mcp App 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 Build Mcp App 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 Build Mcp App 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.

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.

Testing in Claude: Add the server as a custom connector in claude.ai (via a Cloudflare tunnel for local dev) — this exercises the real iframe sandbox and hostContext. See https://claude.com/docs/connectors/building/testing.

Claude host specifics

_meta.ui.* keyWhereEffect
resourceUritoolWhich ui:// resource the host renders for this tool's results.
visibility: ["app"]toolHide a widget-only helper tool (e.g. geometry/image fetcher called via callServerTool) from Claude's tool list.
prefersBorder: falseresourceDrop the host's outer card border (mobile).
csp.{connectDomains, resourceDomains, baseUriDomains}resourceDeclare external origins; default is block-all. frameDomains is currently restricted in Claude.
  • hostContext.safeAreaInsets: {top, right, bottom, left} (px) — honor these for notches and the composer overlay.
  • Directory submission requires OAuth or authless (none) — static bearer is private-deploy only and blocks listing — plus tool annotations and 3–5 PNG screenshots; see references/directory-checklist.md.

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:

SignalWidget type
Tool needs structured input Claude can't reliably inferForm
User must pick from a list Claude can't rank (files, contacts, records)Picker / table
Destructive or billable action needs explicit confirmationConfirm dialog
Output is spatial or visual (charts, maps, diffs, previews)Display widget
Long-running job the user wants to watchProgress / 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.

NeedElicitationWidget
Confirm yes/nooverkill
Pick from short enumoverkill
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.

typescript
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",
  annotations: { title: "Pick Contact", readOnlyHint: true },
  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.

html
<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.

MethodDirectionUse for
app.ontoolresult = fnHost → widgetReceive the tool's return value
app.ontoolinput = fnHost → widgetReceive the tool's input args (what Claude passed)
app.sendMessage({...})Widget → hostInject a message into the conversation
app.updateModelContext({...})Widget → hostUpdate context silently (no visible message)
app.callServerTool({name, arguments})Widget → serverCall another tool on your server
app.openLink({url})Widget → hostOpen a URL in a new tab (sandbox blocks window.open)
app.getHostContext() / app.onhostcontextchangedHost → widgetTheme, host CSS vars, containerDimensions, displayMode, deviceCapabilities
app.requestDisplayMode({mode})Widget → hostAsk for inline / pip / fullscreen
app.downloadFile({name, mimeType, content})Widget → hostHost-mediated download (base64 content)
new App(info, caps, {autoResize: true})Iframe height tracks rendered content

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:

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

Server (src/server.ts):

typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js";
import { registerAppTool, registerAppResource, RESOURCE_MIME_TYPE }
  from "@modelcontextprotocol/ext-apps/server";
import express from "express";
import { readFileSync } from "node:fs";
import { createRequire } from "node:module";
import { z } from "zod";

const require = createRequire(import.meta.url);
const server = new McpServer({ name: "contact-picker", version: "1.0.0" });

// Inline the ext-apps browser bundle into the widget HTML.
// The iframe CSP blocks CDN script fetches — bundling is mandatory.
const bundle = readFileSync(
  require.resolve("@modelcontextprotocol/ext-apps/app-with-deps"), "utf8",
).replace(/export\{([^}]+)\};?\s*$/, (_, body) =>
  "globalThis.ExtApps={" +
  body.split(",").map((p) => {
    const [local, exported] = p.split(" as ").map((s) => s.trim());
    return `${exported ?? local}:${local}`;
  }).join(",") + "};",
);
const pickerHtml = readFileSync("./widgets/picker.html", "utf8")
  .replace("/*__EXT_APPS_BUNDLE__*/", () => bundle);

registerAppTool(server, "pick_contact", {
  description: "Open an interactive contact picker. User selects one contact.",
  annotations: { title: "Pick Contact", readOnlyHint: true },
  inputSchema: { filter: z.string().optional().describe("Name/email prefix filter") },
  _meta: { ui: { resourceUri: "ui://widgets/picker.html" } },
}, async ({ filter }) => {
  const contacts = await db.contacts.search(filter ?? "");
  return { content: [{ type: "text", text: JSON.stringify(contacts) }] };
});

registerAppResource(server, "Contact Picker", "ui://widgets/picker.html", {},
  async () => ({
    contents: [{ uri: "ui://widgets/picker.html", mimeType: RESOURCE_MIME_TYPE, text: pickerHtml }],
  }),
);

const app = express();
app.use(express.json());
app.post("/mcp", async (req, res) => {
  const transport = new StreamableHTTPServerTransport({ sessionIdGenerator: undefined });
  res.on("close", () => transport.close());
  await server.connect(transport);
  await transport.handleRequest(req, res, req.body);
});
app.listen(process.env.PORT ?? 3000);

For local-only widget apps (driving a desktop app, reading local files), swap the transport to StdioServerTransport and package via the build-mcpb skill.

Widget (widgets/picker.html):

html
<!doctype html>
<meta charset="utf-8" />
<style>
  body { font: 14px system-ui; margin: 0; }
  ul { list-style: none; padding: 0; margin: 0; max-height: 300px; overflow-y: auto; }
  li { padding: 10px 14px; cursor: pointer; border-bottom: 1px solid #eee; }
  li:hover { background: #f5f5f5; }
  .sub { color: #666; font-size: 12px; }
</style>
<ul id="list"></ul>
<script type="module">
/*__EXT_APPS_BUNDLE__*/
const { App } = globalThis.ExtApps;
(async () => {
  const app = new App({ name: "ContactPicker", version: "1.0.0" }, {});
  const ul = document.getElementById("list");

  app.ontoolresult = ({ content }) => {
    const contacts = JSON.parse(content[0].text);
    ul.innerHTML = "";
    for (const c of contacts) {
      const li = document.createElement("li");
      li.innerHTML = `<div>${c.name}</div><div class="sub">${c.email}</div>`;
      li.addEventListener("click", () => {
        app.sendMessage({
          role: "user",
          content: [{ type: "text", text: `Selected contact: ${c.id} (${c.name})` }],
        });
      });
      ul.append(li);
    }
  };

  await app.connect();
})();
</script>

See references/widget-templates.md for more widget shapes.


Design notes that save you a rewrite

One widget per tool. Resist the urge to build one mega-widget that does everything. One tool → one focused widget → one clear result shape. Claude reasons about these far better.

Tool description must mention the widget. Claude only sees the tool description when deciding what to call. "Opens an interactive picker" in the description is what makes Claude reach for it instead of guessing an ID.

Widgets are optional at runtime. Hosts that don't support the apps surface simply ignore _meta.ui and render the tool's text content normally. Since your tool handler already returns meaningful text/JSON (the widget's data), degradation is automatic — Claude sees the data directly instead of via the widget.

Don't block on widget results for read-only tools. A widget that just displays data (chart, preview) shouldn't require a user action to complete. Return the display widget and a text summary in the same result so Claude can continue reasoning without waiting.

Layout-fork by item count, not by tool count. If one use case is "show one result in detail" and another is "show many results side-by-side", don't make two tools — make one tool that accepts items[], and let the widget pick a layout: items.length === 1 → detail view, > 1 → carousel. Keeps the server schema simple and lets Claude decide count naturally.

Put Claude's reasoning in the payload. A short note field on each item (why Claude picked it) rendered as a callout on the card gives users the reasoning inline with the choice. Mention this field in the tool description so Claude populates it.

Normalize image shapes server-side. If your data source returns images with wildly varying aspect ratios, rewrite to a predictable variant (e.g. square-bounded) before fetching for the data-URL inline. Then give the widget's image container a fixed aspect-ratio + object-fit: contain so everything sits centered.

Follow host theme. app.getHostContext()?.theme (after connect()) plus app.onhostcontextchanged for live updates. Toggle a .dark class on <html>, keep colors in CSS custom props with a :root.dark {} override block, set color-scheme. Disable mix-blend-mode: multiply in dark — it makes images vanish.


Testing

Claude Desktop — current builds still require the command/args config shape (no native "type": "http"). Wrap with mcp-remote and force http-only transport so the SSE probe doesn't swallow widget-capability negotiation:

json
{
  "mcpServers": {
    "my-server": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:3000/mcp",
               "--allow-http", "--transport", "http-only"]
    }
  }
}

Desktop caches UI resources aggressively. After editing widget HTML, fully quit (⌘Q / Alt+F4, not window-close) and relaunch to force a cold resource re-fetch.

Headless JSON-RPC loop — fast iteration without clicking through Desktop:

bash
# test.jsonl — one JSON-RPC message per line
{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"t","version":"0"}}}
{"jsonrpc":"2.0","method":"notifications/initialized"}
{"jsonrpc":"2.0","id":2,"method":"tools/list"}
{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"your_tool","arguments":{...}}}

(cat test.jsonl; sleep 10) | npx mcp-remote http://localhost:3000/mcp --allow-http

The sleep keeps stdin open long enough to collect all responses. Parse the jsonl output with jq or a Python one-liner.

Widget dev loop — avoid the ⌘Q-relaunch cycle entirely by serving the inlined widget HTML at a plain GET route with a fake ExtApps shim that fires ontoolresult from a query param:

ts
app.get("/widget-preview", (_req, res) => {
  const shim = `globalThis.ExtApps={applyHostStyleVariables:()=>{},App:class{
    constructor(){this.h={}} ontoolresult;onhostcontextchanged;
    async connect(){const p=new URLSearchParams(location.search).get("payload");
      if(p)this.ontoolresult?.({content:[{type:"text",text:p}]});}
    getHostContext(){return{theme:"light"}}
    sendMessage(m){console.log("sendMessage",m)} updateModelContext(){}
    callServerTool(){return Promise.resolve({content:[]})} openLink(){} downloadFile(){}
  }};`;
  res.type("html").send(widgetHtml.replace("/*__EXT_APPS_BUNDLE__*/", shim));
});

Open http://localhost:3000/widget-preview?payload={"rows":[...]} in a normal browser tab and iterate with ordinary devtools.

Host fallback — use a host without the apps surface (or MCP Inspector) and confirm the tool's text content degrades gracefully.

CSP debugging — open the iframe's own devtools console. CSP violations are the #1 reason widgets silently fail (blank rectangle, no error in the main console). See references/iframe-sandbox.md.


Reference files

  • references/iframe-sandbox.md — CSP/sandbox constraints, the bundle-inlining pattern, image handling, host theming
  • references/widget-templates.md — reusable HTML scaffolds for picker / confirm / progress / display
  • references/apps-sdk-messages.md — the App class API: widget ↔ host ↔ server messaging, lifecycle & supersession
  • references/payload-budgeting.md — host tool-result size caps, prune-then-truncate, heavy assets via callServerTool
  • references/abuse-protection.md — Anthropic egress CIDRs, tiered rate limiting, trust proxy, response caching
  • references/directory-checklist.md — pre-flight for connector-directory submission

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Build Mcp App AI skill do?

This skill should be used when the user wants to build an "MCP app", add "interactive UI" or "widgets" to an MCP server, "render components in chat", build "MCP UI resources", make a tool that shows a "form", "picker", "dashboard" or "confirmation dialog" inline in the conversation, or mentions "apps SDK" in the context of MCP. Use AFTER the build-mcp-server skill has settled the deployment model, or when the user already knows they want UI widgets.

Why use Build Mcp App on TypingMind?

Because you install it once and use it with any model. Build Mcp App 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 Build Mcp App in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/claude-plugins-official/tree/main/plugins/mcp-server-dev/skills/build-mcp-app. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Build Mcp App?

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 Build Mcp App?

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

Is the Build Mcp App AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. 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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