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MCP Use

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mcp-use

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

Publishermcp-use
Repositorymcp-use
LanguageTypeScript
Forks
1.5K
Stars
10.7K
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    MCP Use exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    10.7K stars and 1.5K forks from the linked repository.

[!NOTE] Migrating from v1? Give it to your agent:

text
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

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

Read the prompt →

Start with code

bash
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:

typescript
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:

tsx
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 →

Build

Create the production build:

bash
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:

bash
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:

bash
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.

bash
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.

mermaid
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["❌"]
  inspector["Built-in<br/>Inspector"] inspectorMcp["✅"] inspectorFast["✅"] inspectorOfficial["❌"] inspectorXmcp["❌"] inspectorSkybridge["◐ Limited"] inspectorHandler["❌"]

  classDef metricLabel fill:#6e76811a,font-weight:bold
  classDef brand fill:#2ea04333,stroke:#2da44e,stroke-width:3px,font-weight:bold
  classDef header fill:#6e76811a,font-weight:bold
  classDef value fill:#6e76810f,stroke-width:1px
  classDef leader fill:#2ea0432e,stroke:#2da44e,stroke-width:2px,font-weight:bold
  classDef partial fill:#bb80092e,stroke:#bf8700,stroke-width:2px,font-weight:bold
  classDef unavailable fill:#6e76810f,opacity:0.72

  class metric,speed,install,packages,views,nativeViews,oauth,protocol,screenshot,tunnel,inspector metricLabel
  class mcp brand
  class fastmcp,official,xmcp,skybridge,handler header
  class speedFast,speedOfficial,speedXmcp,speedSkybridge,speedHandler,installFast,installOfficial,installXmcp,installSkybridge,installHandler,packagesFast,packagesOfficial,packagesXmcp,packagesSkybridge,packagesHandler value
  class speedMcp,installMcp,packagesMcp,viewsMcp,viewsFast,viewsXmcp,viewsSkybridge,nativeViewsMcp,nativeViewsFast,oauthMcp,oauthXmcp,oauthSkybridge,protocolMcp,protocolFast,protocolOfficial,screenshotMcp,tunnelMcp,tunnelSkybridge,inspectorMcp,inspectorFast leader
  class oauthFast,viewsOfficial,oauthOfficial,inspectorSkybridge partial
  class viewsHandler,nativeViewsOfficial,nativeViewsXmcp,nativeViewsSkybridge,nativeViewsHandler,oauthHandler,protocolXmcp,protocolSkybridge,protocolHandler,screenshotFast,screenshotOfficial,screenshotXmcp,screenshotSkybridge,screenshotHandler,tunnelFast,tunnelOfficial,tunnelXmcp,tunnelHandler,inspectorOfficial,inspectorXmcp,inspectorHandler unavailable

* Includes @modelcontextprotocol/ext-apps, Vite, and zod for an MCP Apps-capable stack.

Install rows compare custom React MCP App development stacks. FastMCP therefore includes the Apps extension, React, Vite React plugin, Vite, TypeScript, and zod rather than only its narrower server-side component workflow. Size is actual node_modules disk usage after a normal npm install, including required peer dependencies.

Read the detailed benchmark report →

Examples

Remix a complete MCP App, inspect the source, or deploy it as a starting point:

PreviewAppWhat it demonstrates
Chart BuilderStructured data rendered as interactive charts · Open demo
Diagram BuilderCreate and edit diagrams through MCP tools · Open demo
Maps ExplorerSearch, detail tools, and an interactive map view · Open demo

Browse all TypeScript examples →

Ecosystem

PackageUse it for
mcp-useTypeScript v2 server framework, React views, and CLI
@mcp-use/clientConnect to MCP servers from Node.js, browsers, React, and sandboxes
@mcp-use/agentBuild model-powered agents on top of MCP clients
@mcp-use/inspectorInspect and debug MCP servers and apps
@mcp-use/tunnelExpose local HTTP, WebSocket, and MCP servers through the managed relay
create-mcp-use-appScaffold servers and interactive apps
mcp-use for PythonBuild Python MCP servers, clients, and agents

Protocol conformance

Security and community

Contributors

Built by Pietro, Luigi, Enrico, and the mcp-use community.

Installation

TypingMind
{
  "mcpServers": {
    "mcp-use": {
      "command": "python",
      "args": [
        "-m",
        "mcp_use.cli"
      ]
    }
  }
}

Use MCP Use MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once MCP Use is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add MCP Use as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the MCP Use MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "mcp-use": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the MCP Use MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the MCP Use MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use MCP Use to help me with this task?
MCP Use
Sure. I read it.
Here is what I found using MCP Use.

Frequently asked questions

What is the MCP Use MCP server used for?

MCP Use is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use MCP Use MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use MCP Use with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use MCP Use MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With MCP Use connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect MCP Use MCP to TypingMind?

MCP Use runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does MCP Use MCP provide in TypingMind?

MCP Use exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use MCP Use MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If MCP Use requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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