by mcp-use
The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.
# Add to your Claude Code skills
git clone https://github.com/mcp-use/mcp-useLast scanned: 4/18/2026
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"status": "PASSED",
"scannedAt": "2026-04-18T05:41:41.529Z",
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}mcp-use is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by mcp-use. The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents. It has 10,526 GitHub stars.
Yes. mcp-use passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/mcp-use/mcp-use" and add it to your Claude Code skills directory (see the Installation section above).
mcp-use is primarily written in TypeScript. It is open-source under mcp-use on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh mcp-use against similar tools.
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This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
[!NOTE] Migrating from v1? Give it to your agent:
Migrate this mcp-use project to v2 following https://docs.mcp-use.com/v2/typescript/server/migration
Build an MCP server: https://mcp-use.com/prompt.md
npx -y create-mcp-use-app@latest
Run npm run dev in the generated project · open http://localhost:3000/mcp/inspector
The scaffold gives you the server, TypeScript configuration, development scripts, Inspector, and a React view pipeline. Start it once and the MCP endpoint also serves a client-ready landing page with its connection URL and setup instructions.
Replace its index.ts with a view-bound tool like this:
import { MCPServer } from "mcp-use";
import { z } from "zod";
const server = new MCPServer({
name: "weather-app",
title: "Weather App",
version: "1.0.0",
});
const weatherInput = z.object({
city: z.string().describe("City to look up"),
});
const weatherOutput = z.object({
city: z.string(),
temperature: z.number(),
conditions: z.string(),
});
export const getWeather = server.tool(
{
name: "get-weather",
title: "Get weather",
description: "Get the current weather for a city",
inputSchema: weatherInput,
outputSchema: weatherOutput,
view: { name: "weather-card" },
annotations: {
readOnlyHint: true,
destructiveHint: false,
openWorldHint: true,
},
},
async ({ city }) => {
const weather = {
city,
temperature: 22,
conditions: "Sunny",
};
return {
content: [
{
type: "text",
text: `Weather in ${city}: ${weather.conditions}, ${weather.temperature}°C`,
},
],
structuredContent: weather,
};
},
);
export default server;
Create views/weather-card/view.tsx. The directory name matches view.name on the tool:
import { useCallTool, useToolContext } from "mcp-use/react";
export default function WeatherCard() {
const { status, toolOutput, toolInput } =
useToolContext<"get-weather">();
const refresh = useCallTool("get-weather");
if (status === "pending") {
return <p>Checking the weather in {toolInput?.city ?? "your city"}…</p>;
}
if (status === "error") return <p>Could not load the weather.</p>;
const weather = refresh.data?.structuredContent ?? toolOutput;
return (
<main style={{ padding: 24 }}>
<h2>{weather.city}</h2>
<p>
{weather.temperature}°C · {weather.conditions}
</p>
<button
disabled={refresh.isPending}
onClick={() => void refresh.callTool({ city: weather.city })}
>
{refresh.isPending ? "Refreshing…" : "Refresh"}
</button>
{refresh.error && <p>{refresh.error.message}</p>}
</main>
);
}
Create the production build:
npm run build
Start development mode to serve the MCP endpoint at http://localhost:3000/mcp. The Inspector is automatically available at http://localhost:3000/mcp/inspector:
npm run dev
Start a tunnel from the Inspector UI or run mcp-use dev --tunnel to get a public URL for your local MCP server and test it with ChatGPT and Claude before deployment. Learn more about tunneling →
Inspect the same server headlessly from the terminal, invoke representative tools, and capture a View screenshot:
npm install --save-dev @mcp-use/client
npx mcp-use client connect local http://localhost:3000/mcp
npx mcp-use client local tools list
npx mcp-use client local tools call get-weather city=Tokyo
npx mcp-use screenshot \
--server local \
--tool get-weather \
city=Tokyo \
--output weather-card.png
Ship to Manufact and get observability, analytics, evals, submission readiness, and Git-based preview environments for free.
npm run deploy
Prefer to run it yourself? Follow the self-hosting guide →.
mcp-use builds on the official TypeScript SDK v2 and adds first-class Views, typed tool-to-UI contracts, an optimized stateless runtime, the Inspector, screenshot verification, agent-first CLI workflows, and deployment.
block-beta
columns 7
metric["Metric"] mcp["mcp-use v2"] fastmcp["FastMCP TS"] official["Official SDK v2*"] xmcp["xmcp"] skybridge["Skybridge"] handler["mcp-handler"]
speed["Speed"] speedMcp["10,982 ops/s"] speedFast["6,628 ops/s"] speedOfficial["8,050 ops/s"] speedXmcp["6,585 ops/s"] speedSkybridge["8,116 ops/s"] speedHandler["6,324 ops/s"]
install["MCP App<br/>dev stack"] installMcp["74.4 MiB"] installFast["122.5 MiB"] installOfficial["99.0 MiB"] installXmcp["121.9 MiB"] installSkybridge["137.5 MiB"] installHandler["388.0 MiB"]
packages["Installed<br/>packages"] packagesMcp["51"] packagesFast["180"] packagesOfficial["119"] packagesXmcp["171"] packagesSkybridge["300"] packagesHandler["130"]
views["Views"] viewsMcp["✅"] viewsFast["✅"] viewsOfficial["◐ Extension"] viewsXmcp["✅"] viewsSkybridge["✅"] viewsHandler["❌"]
nativeViews["Native Views<br/>on MCP 2026"] nativeViewsMcp["✅"] nativeViewsFast["✅"] nativeViewsOfficial["❌"] nativeViewsXmcp["❌"] nativeViewsSkybridge["❌"] nativeViewsHandler["❌"]
oauth["One-line<br/>OAuth adapters"] oauthMcp["✅"] oauthFast["◐ Provider/proxy"] oauthOfficial["◐ Primitives"] oauthXmcp["✅"] oauthSkybridge["✅"] oauthHandler["❌"]
protocol["MCP 2026<br/>protocol"] protocolMcp["✅"] protocolFast["✅"] protocolOfficial["✅"] protocolXmcp["❌"] protocolSkybridge["❌"] protocolHandler["❌"]
screenshot["Built-in View<br/>screenshot CLI"] screenshotMcp["✅"] screenshotFast["❌"] screenshotOfficial["❌"] screenshotXmcp["❌"] screenshotSkybridge["❌"] screenshotHandler["❌"]
tunnel["Built-in<br/>tunneling"] tunnelMcp["✅"] tunnelFast["❌"] tunnelOfficial["❌"] tunnelXmcp["❌"] tunnelSkybridge["✅"] tunnelHandler["❌"