Deploy, Test & Monitor MCP Servers for ChatGPT & Claude

[!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

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:

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:

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 your first MCP App →

Create ChatGPT Plugin Extensions

Build ChatGPT Plugin Extensions with interactive Views that users can open alongside their conversation. Use useModelContext to let them add content from a View to the conversation, such as a book to discuss or analytics to explore.

import { useModelContext } from "mcp-use/react";

export default function AddBook() {
  const { add, pending, error } = useModelContext();

  async function shareBook() {
    try {
      await add("selected-book", {
        type: "text",
        title: "The Great Gatsby",
        text: "Selected book: The Great Gatsby by F. Scott Fitzgerald",
      });
    } catch (error) {
      console.error("Could not share the book", error);
    }
  }

  return (
    <>
      <button disabled={pending} onClick={shareBook}>
        {pending ? "Adding…" : "Add book to chat"}
      </button>
      {error && <p role="alert">{error.message}</p>}
    </>
  );
}

Little Bookshop

https://github.com/user-attachments/assets/2f9b56c1-f149-430e-b5b6-a258c08491e6

Manufact Analytics

https://github.com/user-attachments/assets/e488c66e-8f65-4930-8e08-711bdf12ec6e

Explore ChatGPT Plugin Extensions →

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

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 →.

This is the beginning of the README. Read the rest at the original. Read the full README ↗