MCP App Testing

Open Source MCP CLient Library

🌐 MCP Use是一种开源方式，可以将任何LLM连接到MCP工具，并构建具有工具访问权限的自定义代理，而无需使用闭源或应用程序客户端。

💡 让开发人员轻松地将任何LLM连接到web浏览、文件操作等工具。

特性

✨ 主要特点

快速启动

使用pip：

pip install mcp-use

或者从源代码安装：

git clone https://github.com/pietrozullo/mcp-use.git
cd mcp-use
pip install -e .

安装LangChain提供程序

mcp_use通过LangChain与各种LLM提供商合作。您需要为您选择的LLM安装相应的LangChain提供程序包。例如：

# For OpenAI
pip install langchain-openai

# For Anthropic
pip install langchain-anthropic

# For other providers, check the [LangChain chat models documentation](https://python.langchain.com/docs/integrations/chat/)

并将您要使用的提供商的API密钥添加到您的 .env 文件。

OPENAI_API_KEY=
ANTHROPIC_API_KEY=

重要：只有具有工具调用功能的模型才能与mcp_use一起使用。确保您选择的模型支持函数调用或工具使用。

启动您的代理：

import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

async def main():
    # Load environment variables
    load_dotenv()

    # Create configuration dictionary
    config = {
      "mcpServers": {
        "playwright": {
          "command": "npx",
          "args": ["@playwright/mcp@latest"],
          "env": {
            "DISPLAY": ":1"
          }
        }
      }
    }

    # Create MCPClient from configuration dictionary
    client = MCPClient.from_dict(config)

    # Create LLM
    llm = ChatOpenAI(model="gpt-4o")

    # Create agent with the client
    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    # Run the query
    result = await agent.run(
        "Find the best restaurant in San Francisco",
    )
    print(f"\nResult: {result}")

if __name__ == "__main__":
    asyncio.run(main())

您还可以从配置文件中添加服务器配置，如下所示：

client = MCPClient.from_config_file(
        os.path.join("browser_mcp.json")
    )

配置文件示例(browser_mcp.json):

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"],
      "env": {
        "DISPLAY": ":1"
      }
    }
  }
}

有关其他设置、型号等信息，请查看文档。

示例用例

用Playwright浏览网页

import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

async def main():
    # Load environment variables
    load_dotenv()

    # Create MCPClient from config file
    client = MCPClient.from_config_file(
        os.path.join(os.path.dirname(__file__), "browser_mcp.json")
    )

    # Create LLM
    llm = ChatOpenAI(model="gpt-4o")
    # Alternative models:
    # llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
    # llm = ChatGroq(model="llama3-8b-8192")

    # Create agent with the client
    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    # Run the query
    result = await agent.run(
        "Find the best restaurant in San Francisco USING GOOGLE SEARCH",
        max_steps=30,
    )
    print(f"\nResult: {result}")

if __name__ == "__main__":
    asyncio.run(main())

Airbnb搜索

import asyncio
import os
from dotenv import load_dotenv
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient

async def run_airbnb_example():
    # Load environment variables
    load_dotenv()

    # Create MCPClient with Airbnb configuration
    client = MCPClient.from_config_file(
        os.path.join(os.path.dirname(__file__), "airbnb_mcp.json")
    )

    # Create LLM - you can choose between different models
    llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

    # Create agent with the client
    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    try:
        # Run a query to search for accommodations
        result = await agent.run(
            "Find me a nice place to stay in Barcelona for 2 adults "
            "for a week in August. I prefer places with a pool and "
            "good reviews. Show me the top 3 options.",
            max_steps=30,
        )
        print(f"\nResult: {result}")
    finally:
        # Ensure we clean up resources properly
        if client.sessions:
            await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(run_airbnb_example())

配置文件示例(airbnb_mcp.json):

{
  "mcpServers": {
    "airbnb": {
      "command": "npx",
      "args": ["-y", "@openbnb/mcp-server-airbnb"]
    }
  }
}

Blender 3D创建

import asyncio
from dotenv import load_dotenv
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient

async def run_blender_example():
    # Load environment variables
    load_dotenv()

    # Create MCPClient with Blender MCP configuration
    config = {"mcpServers": {"blender": {"command": "uvx", "args": ["blender-mcp"]}}}
    client = MCPClient.from_dict(config)

    # Create LLM
    llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

    # Create agent with the client
    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    try:
        # Run the query
        result = await agent.run(
            "Create an inflatable cube with soft material and a plane as ground.",
            max_steps=30,
        )
        print(f"\nResult: {result}")
    finally:
        # Ensure we clean up resources properly
        if client.sessions:
            await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(run_blender_example())

配置文件支持

MCP Use支持从配置文件初始化，使管理和切换不同的MCP服务器设置变得容易：

import asyncio
from mcp_use import create_session_from_config

async def main():
    # Create an MCP session from a config file
    session = create_session_from_config("mcp-config.json")

    # Initialize the session
    await session.initialize()

    # Use the session...

    # Disconnect when done
    await session.disconnect()

if __name__ == "__main__":
    asyncio.run(main())

HTTP连接示例

MCP Use现在支持HTTP连接，允许您连接到在特定HTTP端口上运行的MCP服务器。此功能对于与基于web的MCP服务器集成特别有用。

以下是一个如何使用HTTP连接功能的示例：

import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

async def main():
    """Run the example using a configuration file."""
    # Load environment variables
    load_dotenv()

    config = {
        "mcpServers": {
            "http": {
                "url": "http://localhost:8931/sse"
            }
        }
    }

    # Create MCPClient from config file
    client = MCPClient.from_dict(config)

    # Create LLM
    llm = ChatOpenAI(model="gpt-4o")

    # Create agent with the client
    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    # Run the query
    result = await agent.run(
        "Find the best restaurant in San Francisco USING GOOGLE SEARCH",
        max_steps=30,
    )
    print(f"\nResult: {result}")

if __name__ == "__main__":
    # Run the appropriate example
    asyncio.run(main())

此示例演示了如何连接到在特定HTTP端口上运行的MCP服务器。请确保在运行此示例之前启动MCP服务器。

多服务器支持

MCP Use支持同时使用多个MCP服务器，允许您在单个代理中组合来自不同服务器的工具。这对于需要多种功能的复杂任务非常有用，例如网页浏览与文件操作或3D建模相结合。

配置

您可以在配置文件中配置多个服务器：

{
  "mcpServers": {
    "airbnb": {
      "command": "npx",
      "args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"]
    },
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"],
      "env": {
        "DISPLAY": ":1"
      }
    }
  }
}

用法

这 MCPClient 类提供了几种管理多个服务器的方法：

import asyncio
from mcp_use import MCPClient, MCPAgent
from langchain_anthropic import ChatAnthropic

async def main():
    # Create client with multiple servers
    client = MCPClient.from_config_file("multi_server_config.json")

    # Create agent with the client
    agent = MCPAgent(
        llm=ChatAnthropic(model="claude-3-5-sonnet-20240620"),
        client=client
    )

    try:
        # Run a query that uses tools from multiple servers
        result = await agent.run(
            "Search for a nice place to stay in Barcelona on Airbnb, "
            "then use Google to find nearby restaurants and attractions."
        )
        print(result)
    finally:
        # Clean up all sessions
        await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(main())

工具访问控制

MCP使用允许您限制代理可用的工具，从而提供更好的安全性和对代理功能的控制：

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

async def main():
    # Create client
    client = MCPClient.from_config_file("config.json")

    # Create agent with restricted tools
    agent = MCPAgent(
        llm=ChatOpenAI(model="gpt-4"),
        client=client,
        disallowed_tools=["file_system", "network"]  # Restrict potentially dangerous tools
    )

    # Run a query with restricted tool access
    result = await agent.run(
        "Find the best restaurant in San Francisco"
    )
    print(result)

    # Clean up
    await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(main())

路线图

[x] Multiple Servers at once

[x] Test remote connectors (http, ws)

[ ] ...

贡献

我们热爱贡献！对于bug或功能请求，请随时打开问题。

需求

• Python 3.11+
• MCP实现（如Playwright MCP）
• LangChain和适当的模型库（OpenAI、Anthropic等）

引用

如果您在研究或项目中使用MCP use，请引用：

@software{mcp_use2025,
  author = {Zullo, Pietro},
  title = {MCP-Use: MCP Library for Python},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/pietrozullo/mcp-use}
}

许可证

麻省理工学院