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Installing mcp_use

Prerequisites: Please install Python (version 3.11 or higher) before proceeding.

1

Install the library

Install mcp_use using your preferred package manager:

pip install mcp-use
2

Install LLM provider

Choose and install your preferred LangChain provider:

pip install langchain-openai
3

Set up environment

Create a .env file for your API keys:

OPENAI_API_KEY=your_openai_key_here
ANTHROPIC_API_KEY=your_anthropic_key_here
GROQ_API_KEY=your_groq_key_here
GOOGLE_API_KEY=your_google_key_here
4

Verify installation

Test your installation with a simple script:

test_install.py
from mcp_use import MCPAgent, MCPClient
print("mcp_use installed successfully!")

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Development Installation

If you want to contribute or use the latest features, install from source:

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

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Installing MCP Servers

mcp_use connects to MCP servers that provide the actual tools. Here are some popular ones:

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Playwright (Web Scraping)

npx @playwright/mcp@latest

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Filesystem Server

pip install mcp-server-filesystem

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SQLite Server

pip install mcp-server-sqlite

Check out the Awesome MCP Servers repository for a comprehensive list of available servers.

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Environment Setup

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Using Virtual Environments

It’s recommended to use virtual environments to avoid dependency conflicts:

python -m venv mcp_env
source mcp_env/bin/activate  # On Windows: mcp_env\Scripts\activate
pip install mcp-use langchain-openai

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Environment Variables

Create a .env file in your project root:

.env
# LLM Provider Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GROQ_API_KEY=gsk_...
GOOGLE_API_KEY=AI...

# Optional: Logging level
LOG_LEVEL=INFO

# Optional: MCP server paths
MCP_SERVER_PATH=/path/to/mcp/servers

Load environment variables in your Python scripts:

from dotenv import load_dotenv
import os

load_dotenv()

# Your API keys are now available as environment variables
openai_key = os.getenv("OPENAI_API_KEY")

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Verification

Verify your installation works correctly:

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

async def verify_installation():
    load_dotenv()

    # Simple configuration for testing
    config = {
        "mcpServers": {
            "test": {
                "command": "echo",
                "args": ["Hello from MCP!"]
            }
        }
    }

    try:
        client = MCPClient.from_dict(config)
        print("✅ MCPClient created successfully")

        llm = ChatOpenAI(model="gpt-3.5-turbo")
        print("✅ LLM initialized successfully")

        agent = MCPAgent(llm=llm, client=client)
        print("✅ MCPAgent created successfully")

        print("\n🎉 Installation verified! You're ready to use mcp_use.")

    except Exception as e:
        print(f"❌ Verification failed: {e}")
        print("Please check your installation and API keys.")

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

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Next Steps

Quick Start

Follow our quickstart guide to build your first agent

Configuration

Learn how to configure MCP servers

LLM Integration

Explore different LLM providers and their setup

Examples

Browse real-world examples and use cases

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Troubleshooting

Tool Calling Required: Only models with tool calling capabilities can be used with mcp_use. Make sure your chosen model supports function calling or tool use.