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Built with ❤️ by Krishna Goyal

    Mcp Use

    Mcp-use is the easiest way to interact with mcp servers with custom agents TypeScript-based implementation. Trusted by 8100+ developers.

    8,149 stars
    TypeScript
    Updated Nov 4, 2025
    agent
    agents
    ai
    mcp
    mcp-client
    model-context-protocol
    model-context-protocol-client
    model-context-protocol-sdk
    python

    Table of Contents

    • About
    • Documentation
    • Skills for Coding Agents
    • Quickstart: MCP Servers and MCP Apps
    • <img src="./static/typescript.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> TypeScript
    • MCP Apps
    • <img src="./static/python.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> Python
    • Inspector
    • Deploy
    • Package Overview
    • Python Packages
    • TypeScript Packages
    • Also: MCP Agent & Client
    • <img src="./static/python.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> Python
    • <img src="./static/typescript.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> TypeScript
    • <img src="./static/python.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> Python
    • <img src="./static/typescript.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> TypeScript
    • Conformance to Model Context Protocol
    • Community & Support
    • Star History
    • Contributors
    • Core Contributors

    Table of Contents

    • About
    • Documentation
    • Skills for Coding Agents
    • Quickstart: MCP Servers and MCP Apps
    • <img src="./static/typescript.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> TypeScript
    • MCP Apps
    • <img src="./static/python.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> Python
    • Inspector
    • Deploy
    • Package Overview
    • Python Packages
    • TypeScript Packages
    • Also: MCP Agent & Client
    • <img src="./static/python.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> Python
    • <img src="./static/typescript.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> TypeScript
    • <img src="./static/python.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> Python
    • <img src="./static/typescript.svg" height="14" style="margin-right:4px; top:-1px; position:relative;" align="center" /> TypeScript
    • Conformance to Model Context Protocol
    • Community & Support
    • Star History
    • Contributors
    • Core Contributors

    Documentation

    &nbsp;

    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:

    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**

    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 ;
    
      return (
        
      );
    };
    
    export default WeatherDisplay;

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

    Visit **MCP Apps Documentation**

    ---

    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**

    ---

    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**

    ---

    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 — production-ready with observability, metrics, logs, and branch-deployments.

    ---

    Package Overview

    This monorepo contains multiple packages for both Python and TypeScript:

    Python Packages

    PackageDescriptionVersion
    mcp-useComplete MCP server and MCP agent SDKPyPI

    TypeScript Packages

    PackageDescriptionVersion
    mcp-useCore framework for MCP servers, MCP apps, and MCP agentsnpm
    @mcp-use/cliBuild tool with hot reload and auto-inspectornpm
    @mcp-use/inspectorWeb-based previewer and debugger for MCP serversnpm
    create-mcp-use-appProject scaffolding toolnpm

    ---

    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**

    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**

    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**

    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**

    ---

    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

    Star History Chart

    ---

    Contributors

    Thanks to all our amazing contributors!

    Core Contributors

    1. Pietro (@pietrozullo)

    2. Luigi (@pederzh)

    3. Enrico (@tonxxd)

    ---

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