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mcp-use / MCP Servers

mcp-use

The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.

  • 10.8k estrellas
  • 1.5k forks
  • 90 issues abiertos
  • Lenguaje: TypeScript
  • Licencia: MIT
  • actualizado

Instalación

local · npx
Claude Code CLI
claude mcp add mcp-use -- npx -y create-mcp-use-app
  1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
  2. Replace any <placeholder> values with your API keys or paths.
  3. Restart Claude. The MCP server and its tools appear automatically.

Nota editorial

mcp-use is a full-stack framework for building and deploying Model Context Protocol servers and interactive MCP Apps, available as both a TypeScript npm package and a Python PyPI package. Developers use the SDK to define MCP tools with Zod schemas, then optionally attach React widget components that render inside Claude, ChatGPT, and other MCP clients without rewriting code per platform. A built-in MCP Inspector, available both online and as an open-source package, lets developers test and debug servers locally at a `/inspector` endpoint during development. The `npx create-mcp-use-app` scaffolding command generates a ready-to-run project, and widgets placed in a `resources/` directory are auto-discovered without manual registration. Production deployment is handled through Manufact MCP Cloud, which adds observability, metrics, logs, and branch deployments connected directly from a GitHub repository. Claude Code users can install a dedicated skill via skills.sh to accelerate MCP App development. The framework suits developers building tools that surface structured, interactive UI inside AI chat interfaces.

README

Resumen de MCP Servers

README completo en GitHub

The TypeScript framework for MCP Build, test, and ship MCP servers, ChatGPT plugins, Claude connectors

Fully Typed, native Views and MCP Apps support, built-in Inspector and first class Agent experience.

Documentation · Inspector · Examples · Deploy

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

Read the migration guide →

Get started

Start with your agent

Build an MCP server: https://mcp-use.com/prompt.md

Read the prompt →

Start with code

npx -y create-mcp-use-app@latest

Run npm run dev in the generated project · open http://localhost:3000/mcp/inspector

TS Docs

Everything you need to ship MCP

Fully typed Zod schemas flow from tools to structured results, View props, and tool calls. Native Views Bind React Views directly to tools and ship interactive apps without custom extension wiring. Agent-first and headless Scaffold, invoke, inspect, screenshot, and deploy through your agent. Built-in debugging tools Inspect tools and Views in the browser or headlessly through the CLI.

Quickstart

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:

index.ts — Server entry file for tool definition and metadata

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;

Explore MCP server tools →

Add Views to your tools

Create views/weather-card/view.tsx. The directory name matches view.name on the tool:

view.tsx — Return a view from your tools: React weather card

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>
  );
}

Build interactive UI experiences within ChatGPT with mcp-use.

Build your first MCP App →

Build

Create the production build:

npm run build

Inspect

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

Invoke tools, validate inputs, and inspect interactive Views in the same development loop.

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

Deploy

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 →.

How mcp-use compares

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["❌"]
Topics
agent-pluginsagentic-frameworkaiapps-sdkchatgptclaude-codeclaude-connectorsllmsmcpmcp-appsmcp-clientmcp-gatewaymcp-inspectormcp-servermcp-serversmcp-toolsmcp-uimodel-context-protocolmodelcontextprotocolskills
Preguntas frecuentes

Lo que la gente pregunta sobre mcp-use

¿Qué es mcp-use/mcp-use?

mcp-use/mcp-use es mcp servers para el ecosistema de Claude AI. The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents. Tiene 10.8k estrellas en GitHub y su última actualización registrada es del 2026-10-22.

¿Cómo se instala mcp-use?

Puedes instalar mcp-use clonando el repositorio (https://github.com/mcp-use/mcp-use) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar mcp-use/mcp-use?

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¿Quién mantiene mcp-use/mcp-use?

mcp-use/mcp-use es mantenido por mcp-use. La última actividad registrada en GitHub es del 2026-10-22, con 90 issues abiertos.

¿Hay alternativas a mcp-use?

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