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https://github.com/mcp-use/mcp-use

The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.
https://github.com/mcp-use/mcp-use

agentic-framework ai apps-sdk chatgpt claude-code llms mcp mcp-apps mcp-client mcp-gateway mcp-host mcp-inspector mcp-server mcp-servers mcp-tools mcp-ui model-context-protocol modelcontextprotocol openclaw skills

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

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##  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](https://www.npmjs.com/package/mcp-use) | [py](https://pypi.org/project/mcp_use/)): MCP Servers and MCP Apps

- **Preview** on mcp-use MCP Inspector ([online](https://inspector.mcp-use.com/inspector) | [oss](https://github.com/mcp-use/mcp-use/tree/main/libraries/typescript/packages/inspector)): Test and debug your MCP Servers and Apps

- **Deploy** on [Manufact MCP Cloud](https://manufact.com): 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](https://mcp-use.com/docs) or jump to a quickstart ([TypeScript](https://mcp-use.com/docs/typescript/getting-started/quickstart) | [Python](https://mcp-use.com/docs/python/getting-started/quickstart))



### Skills for Coding Agents



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

>

> **[Install mcp-use skill for MCP Apps](https://skills.sh/mcp-use/mcp-use/mcp-apps-builder)**



## Quickstart: MCP Servers and MCP Apps



###  TypeScript



Build your first MCP Server or MPC App:



```bash

npx create-mcp-use-app@latest

```



Or create a server manually:



```typescript

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**](https://mcp-use.com/docs/typescript/server)



## 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:



```typescript

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`:



```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>();

  const isDark = theme === "dark";



  if (isPending) return 
Loading...
;



  return (

    


      
{props.city}


      
{props.temperature}° — {props.conditions}


    


  );

};



export default WeatherDisplay;

```



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



Visit [**MCP Apps Documentation**](https://mcp-use.com/docs/typescript/server/ui-widgets)



---

###  Python



```bash

pip install mcp-use

```



```python

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**](https://mcp-use.com/docs/python/server/index)



---



## Inspector



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



**Auto-included** when using `server.listen()`:



```typescript

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:



```bash

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

```



Visit [**Inspector Documentation**](https://mcp-use.com/docs/inspector/index)



---



## Deploy



Deploy your MCP server to production:



```bash

npx @mcp-use/cli login

npx @mcp-use/cli deploy

```



Or connect your GitHub repo on [manufact.com](https://manufact.com) — production-ready with observability, metrics, logs, and branch-deployments.



---



## Package Overview



This monorepo contains multiple packages for both Python and TypeScript:



### Python Packages



| Package     | Description                           | Version                                                                                 |

| ----------- | ------------------------------------- | --------------------------------------------------------------------------------------- |

| **mcp-use** | Complete MCP server and MCP agent SDK | [![PyPI](https://img.shields.io/pypi/v/mcp_use.svg)](https://pypi.org/project/mcp_use/) |



### TypeScript Packages



| Package                | Description                                     | Version                                                                                                         |

| ---------------------- | ----------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |

| **mcp-use**            | Core framework for MCP servers, MCP apps, and MCP agents | [![npm](https://img.shields.io/npm/v/mcp-use.svg)](https://www.npmjs.com/package/mcp-use)                       |

| **@mcp-use/cli**       | Build tool with hot reload and auto-inspector   | [![npm](https://img.shields.io/npm/v/@mcp-use/cli.svg)](https://www.npmjs.com/package/@mcp-use/cli)             |

| **@mcp-use/inspector** | Web-based previewer and debugger for MCP servers              | [![npm](https://img.shields.io/npm/v/@mcp-use/inspector.svg)](https://www.npmjs.com/package/@mcp-use/inspector) |

| **create-mcp-use-app** | Project scaffolding tool                        | [![npm](https://img.shields.io/npm/v/create-mcp-use-app.svg)](https://www.npmjs.com/package/create-mcp-use-app) |



---



## Also: MCP Agent & Client



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



Build an AI Agent



###  Python



```bash

pip install mcp-use langchain-openai

```



```python

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**](./libraries/python/README.md#quick-start)



###  TypeScript



```bash

npm install mcp-use @langchain/openai

```



```typescript

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**](./libraries/typescript/README.md#-quick-start)



Use MCP Client



###  Python



```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**](./libraries/python/README.md#direct-tool-calls-without-llm)



###  TypeScript



```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**](./libraries/typescript/README.md#basic-usage)



---



## Conformance to Model Context Protocol





    

        

    

        

    

        

    

      




---



## Community & Support



- **Discord**: [Join our community](https://discord.gg/XkNkSkMz3V)

- **GitHub Issues**: [Report bugs or request features](https://github.com/mcp-use/mcp-use/issues)

- **Documentation**: [mcp-use.com/docs](https://mcp-use.com/docs)

- **Website**: [manufact.com](https://manufact.com)

- **X.com**: Follow [Manufact](https://x.com/manufact)

- **Contributing**: See [CONTRIBUTING.md](https://github.com/mcp-use/mcp-use/blob/main/CONTRIBUTING.md)

- **License**: MIT © [MCP-Use Contributors](https://github.com/mcp-use/mcp-use/graphs/contributors)



---



## Star History



[![Star History Chart](https://api.star-history.com/svg?repos=mcp-use/mcp-use&type=Date)](https://www.star-history.com/#mcp-use/mcp-use&Date)



---



## Contributors



Thanks to all our amazing contributors!



### Core Contributors



1. **Pietro** ([@pietrozullo](https://github.com/pietrozullo))

2. **Luigi** ([@pederzh](https://github.com/pederzh))

3. **Enrico** ([@tonxxd](https://github.com/tonxxd))









  




---





  Built with ❤️ by Manufact team and the mcp-use community

  


  San Francisco | Zürich