​
Configuration Overview

mcp_use configuration is organized into two main areas: Client Configuration for connecting to MCP servers, and Agent Configuration for customizing agent behavior and LLM integration.

​
Configuration Architecture

mcp_use follows a clear separation between client-side and agent-side concerns:

Client Configuration

MCPClient Setup

  • MCP server connections
  • Multi-server configurations
  • Sandboxed execution
  • Connection types

Agent Configuration

MCPAgent Setup

  • API key management
  • LLM integration
  • Server manager
  • Tool access control
  • Memory and prompts
  • Adapter usage

​
Quick Start Configuration

For a basic setup, you need both client and agent configuration:

​
1. Client Setup

from mcp_use import MCPClient

# Configure your MCP servers
config = {
    "mcpServers": {
        "playwright": {
            "command": "npx",
            "args": ["@playwright/mcp@latest"],
            "env": {"DISPLAY": ":1"}
        }
    }
}

client = MCPClient.from_dict(config)

​
2. Agent Setup

from mcp_use import MCPAgent
from langchain_openai import ChatOpenAI

# Configure your agent with an LLM
llm = ChatOpenAI(model="gpt-4o")
agent = MCPAgent(llm=llm, client=client)

​
3. Basic Usage

import asyncio

async def main():
    result = await agent.run("Search for information about climate change")
    print(result)

asyncio.run(main())

​
Configuration Paths

1

Client Configuration

Set up your MCPClient to connect to MCP servers. This includes configuring server connections, managing API keys, and setting up multi-server environments.

Start here: Client Configuration Guide →

2

Agent Configuration

Configure your MCPAgent’s behavior, including LLM integration, tool restrictions, memory settings, and custom prompts.

Continue with: Agent Configuration Guide →

3

Advanced Topics

Explore connection types, server management, and LLM integration patterns for complex use cases.

Learn more: Connection Types | Server Manager | LLM Integration

​
Common Configuration Patterns

​
Development Setup

# Simple development configuration
from dotenv import load_dotenv
load_dotenv()

client = MCPClient.from_config_file("dev-config.json")
agent = MCPAgent(
    llm=ChatOpenAI(model="gpt-4o"),
    client=client,
    max_steps=10,
    verbose=True
)

​
Production Setup

# Production configuration with restrictions
agent = MCPAgent(
    llm=ChatOpenAI(model="gpt-4o", temperature=0.1),
    client=client,
    max_steps=30,
    disallowed_tools=["file_system", "shell"],
    use_server_manager=True,
    memory_enabled=True
)

​
Multi-Server Setup

# Complex multi-server configuration
client = MCPClient.from_config_file("multi-server.json")
agent = MCPAgent(
    llm=llm,
    client=client,
    use_server_manager=True,  # Auto-select servers
    system_prompt="You have access to web browsing, file operations, and API tools."
)

​
What’s Next?

Client Configuration

Learn how to configure MCPClient and connect to MCP servers

Agent Configuration

Discover how to customize MCPAgent behavior and LLM integration

Examples

Explore real-world configuration examples

New to mcp_use? Start with the Quickstart Guide for a basic introduction, then return here for detailed configuration options.