About

mcp-use is the fullstack MCP framework to build MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.

• Build with mcp-use SDK (ts | py): MCP Servers and MCP Apps
• Preview on mcp-use MCP Inspector (online | oss): Test and debug your MCP Servers and Apps
• Deploy on Manufact MCP Cloud: Connect your GitHub repo and have your MCP Server and App up and running in production with observability, metrics, logs, branch-deployments, and more

Documentation

Visit our docs or jump to a quickstart (TypeScript | Python)

Skills for Coding Agents

Using Claude Code, Codex, Cursor or other AI coding agents?

Install mcp-use skill for MCP Apps

Quickstart: MCP Servers and MCP Apps

TypeScript

Build your first MCP Server or MPC App:

npx create-mcp-use-app@latest

Or create a server manually:

import { MCPServer, text } from "mcp-use/server";
import { z } from "zod";

const server = new MCPServer({
  name: "my-server",
  version: "1.0.0",
});

server.tool({
  name: "get_weather",
  description: "Get weather for a city",
  schema: z.object({ city: z.string() }),
}, async ({ city }) => {
  return text(`Temperature: 72°F, Condition: sunny, City: ${city}`);
});

await server.listen(3000);
// Inspector at http://localhost:3000/inspector

→ Full TypeScript Server Documentation

MCP Apps

MCP Apps let you build interactive widgets that work across Claude, ChatGPT, and other MCP clients — write once, run everywhere.

Server: define a tool and point it to a widget:

import { MCPServer, widget } from "mcp-use/server";
import { z } from "zod";

const server = new MCPServer({
  name: "weather-app",
  version: "1.0.0",
});

server.tool({
  name: "get-weather",
  description: "Get weather for a city",
  schema: z.object({ city: z.string() }),
  widget: "weather-display", // references resources/weather-display/widget.tsx
}, async ({ city }) => {
  return widget({
    props: { city, temperature: 22, conditions: "Sunny" },
    message: `Weather in ${city}: Sunny, 22°C`,
  });
});

await server.listen(3000);

Widget: create a React component in resources/weather-display/widget.tsx:

import { useWidget, type WidgetMetadata } from "mcp-use/react";
import { z } from "zod";

const propSchema = z.object({
  city: z.string(),
  temperature: z.number(),
  conditions: z.string(),
});

export const widgetMetadata: WidgetMetadata = {
  description: "Display weather information",
  props: propSchema,
};

const WeatherDisplay: React.FC = () => {
  const { props, isPending, theme } = useWidget<z.infer<typeof propSchema>>();
  const isDark = theme === "dark";

  if (isPending) return <div>Loading...</div>;

  return (
    <div style={{
      background: isDark ? "#1a1a2e" : "#f0f4ff",
      borderRadius: 16, padding: 24,
    }}>
      <h2>{props.city}</h2>
      <p>{props.temperature}° — {props.conditions}</p>
    </div>
  );
};

export default WeatherDisplay;

Widgets in resources/ are auto-discovered — no manual registration needed.

Visit MCP Apps Documentation

Python

pip install mcp-use

from typing import Annotated

from mcp.types import ToolAnnotations
from pydantic import Field

from mcp_use import MCPServer

server = MCPServer(name="Weather Server", version="1.0.0")

@server.tool(
    name="get_weather",
    description="Get current weather information for a location",
    annotations=ToolAnnotations(readOnlyHint=True, openWorldHint=True),
)
async def get_weather(
    city: Annotated[str, Field(description="City name")],
) -> str:
    return f"Temperature: 72°F, Condition: sunny, City: {city}"

# Start server with auto-inspector
server.run(transport="streamable-http", port=8000)
# Inspector at http://localhost:8000/inspector

→ Full Python Server Documentation

Inspector

The mcp-use Inspector lets you test and debug your MCP servers interactively.

Auto-included when using server.listen():

server.listen(3000);
// Inspector at http://localhost:3000/inspector

Online when connecting to hosted MCP servers:

Visit https://inspector.mcp-use.com

Standalone: inspect any MCP server:

npx @mcp-use/inspector --url http://localhost:3000/mcp

Visit Inspector Documentation

Deploy

Deploy your MCP server to production:

npx @mcp-use/cli login
npx @mcp-use/cli deploy

Or connect your GitHub repo on manufact.com — production-ready with observability, metrics, logs, and branch-deployments.

Package Overview

Python Packages

| Package | Description | Version | | ----------- | ------------------------------------- | --------------------------------------------------------------------------------------- | | mcp-use | Complete MCP server and MCP agent SDK | |

TypeScript Packages

| Package | Description | Version | | ---------------------- | ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | | mcp-use | Core framework for MCP servers, MCP apps, and MCP agents | | | @mcp-use/cli | Build tool with hot reload and auto-inspector | | | @mcp-use/inspector | Web-based previewer and debugger for MCP servers | | | create-mcp-use-app | Project scaffolding tool | |

Also: MCP Agent & Client

mcp-use also provides a full MCP Agent and Client implementation.

Python

pip install mcp-use langchain-openai

import asyncio
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

async def main():
    config = {
        "mcpServers": {
            "filesystem": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
            }
        }
    }

    client = MCPClient.from_dict(config)
    llm = ChatOpenAI(model="gpt-4o")
    agent = MCPAgent(llm=llm, client=client)

    result = await agent.run("List all files in the directory")
    print(result)

asyncio.run(main())

→ Full Python Agent Documentation

TypeScript

npm install mcp-use @langchain/openai

import { ChatOpenAI } from "@langchain/openai";
import { MCPAgent, MCPClient } from "mcp-use";

async function main() {
  const config = {
    mcpServers: {
      filesystem: {
        command: "npx",
        args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
      },
    },
  };

  const client = MCPClient.fromDict(config);
  const llm = new ChatOpenAI({ modelName: "gpt-4o" });
  const agent = new MCPAgent({ llm, client });

  const result = await agent.run("List all files in the directory");
  console.log(result);
}

main();

→ Full TypeScript Agent Documentation

Python

import asyncio
from mcp_use import MCPClient

async def main():
    config = {
        "mcpServers": {
            "calculator": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-everything"]
            }
        }
    }

    client = MCPClient.from_dict(config)
    await client.create_all_sessions()

    session = client.get_session("calculator")
    result = await session.call_tool(name="add", arguments={"a": 5, "b": 3})

    print(f"Result: {result.content[0].text}")
    await client.close_all_sessions()

asyncio.run(main())

→ Python Client Documentation

TypeScript

import { MCPClient } from "mcp-use";

async function main() {
  const config = {
    mcpServers: {
      calculator: {
        command: "npx",
        args: ["-y", "@modelcontextprotocol/server-everything"],
      },
    },
  };

  const client = new MCPClient(config);
  await client.createAllSessions();

  const session = client.getSession("calculator");
  const result = await session.callTool("add", { a: 5, b: 3 });

  console.log(`Result: ${result.content[0].text}`);
  await client.closeAllSessions();
}

main();

→ TypeScript Client Documentation

Conformance to Model Context Protocol

Community & Support

• Discord: Join our community
• GitHub Issues: Report bugs or request features
• Documentation: mcp-use.com/docs
• Website: manufact.com
• X.com: Follow Manufact
• Contributing: See CONTRIBUTING.md
• License: MIT © MCP-Use Contributors

Star History

Contributors

Thanks to all our amazing contributors!

Core Contributors

1. Pietro (@pietrozullo)
2. Luigi (@pederzh)
3. Enrico (@tonxxd)