🌐 MCP-Use is the open source way to connect any LLM to any MCP server and build custom agents that have tool access, without using closed source or application clients.
💡 Let developers easily connect any LLM to tools like web browsing, file operations, and more.
| Feature | Description |
|---|---|
| 🔄 Ease of use | Create your first MCP capable agent you need only 6 lines of code |
| 🤖 LLM Flexibility | Works with any langchain supported LLM that supports tool calling (OpenAI, Anthropic, Groq, LLama etc.) |
| 🌐 HTTP Support | Direct connection to MCP servers running on specific HTTP ports |
| ⚙️ Dynamic Server Selection | Agents can dynamically choose the most appropriate MCP server for a given task from the available pool |
| 🧩 Multi-Server Support | Use multiple MCP servers simultaneously in a single agent |
| 🛡️ Tool Restrictions | Restrict potentially dangerous tools like file system or network access |
With pip:
pip install mcp-useOr install from source:
git clone https://github.com/pietrozullo/mcp-use.git
cd mcp-use
pip install -e .mcp_use works with various LLM providers through LangChain. You'll need to install the appropriate LangChain provider package for your chosen LLM. For example:
# 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/)and add your API keys for the provider you want to use to your .env file.
OPENAI_API_KEY=
ANTHROPIC_API_KEY=Important: Only models with tool calling capabilities can be used with mcp_use. Make sure your chosen model supports function calling or tool use.
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())You can also add the servers configuration from a config file like this:
client = MCPClient.from_config_file(
os.path.join("browser_mcp.json")
)Example configuration file (browser_mcp.json):
{
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}For other settings, models, and more, check out the documentation.
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())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())Example configuration file (airbnb_mcp.json):
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": ["-y", "@openbnb/mcp-server-airbnb"]
}
}
}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())MCP-Use supports initialization from configuration files, making it easy to manage and switch between different MCP server setups:
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())MCP-Use now supports HTTP connections, allowing you to connect to MCP servers running on specific HTTP ports. This feature is particularly useful for integrating with web-based MCP servers.
Here's an example of how to use the HTTP connection feature:
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())This example demonstrates how to connect to an MCP server running on a specific HTTP port. Make sure to start your MCP server before running this example.
MCP-Use allows configuring and connecting to multiple MCP servers simultaneously using the MCPClient. This enables complex workflows that require tools from different servers, such as web browsing combined with file operations or 3D modeling.
You can configure multiple servers in your configuration file:
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"]
},
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}The MCPClient class provides methods for managing connections to multiple servers. When creating an MCPAgent, you can provide an MCPClient configured with multiple servers.
By default, the agent will have access to tools from all configured servers. If you need to target a specific server for a particular task, you can specify the server_name when calling the agent.run() method.
# Example: Manually selecting a server for a specific task
result = await agent.run(
"Search for Airbnb listings in Barcelona",
server_name="airbnb" # Explicitly use the airbnb server
)
result_google = await agent.run(
"Find restaurants near the first result using Google Search",
server_name="playwright" # Explicitly use the playwright server
)For enhanced efficiency and to reduce potential agent confusion when dealing with many tools from different servers, you can enable the Server Manager by setting use_server_manager=True during MCPAgent initialization.
When enabled, the agent intelligently selects the correct MCP server based on the tool chosen by the LLM for a specific step. This minimizes unnecessary connections and ensures the agent uses the appropriate tools for the task.
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,
use_server_manager=True # Enable the Server Manager
)
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())MCP-Use allows you to restrict which tools are available to the agent, providing better security and control over agent capabilities:
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())MCP-Use provides a built-in debug mode that increases log verbosity and helps diagnose issues in your agent implementation.
There are two primary ways to enable debug mode:
Run your script with the DEBUG environment variable set to the desired level:
# Level 1: Show INFO level messages
DEBUG=1 python3.11 examples/browser_use.py
# Level 2: Show DEBUG level messages (full verbose output)
DEBUG=2 python3.11 examples/browser_use.pyThis sets the debug level only for the duration of that specific Python process.
Alternatively you can set the following environment variable to the desired logging level:
export MCP_USE_DEBUG=1 # or 2You can set the global debug flag directly in your code:
import mcp_use
mcp_use.set_debug(1) # INFO level
# or
mcp_use.set_debug(2) # DEBUG level (full verbose output)If you only want to see debug information from the agent without enabling full debug logging, you can set the verbose parameter when creating an MCPAgent:
# Create agent with increased verbosity
agent = MCPAgent(
llm=your_llm,
client=your_client,
verbose=True # Only shows debug messages from the agent
)This is useful when you only need to see the agent's steps and decision-making process without all the low-level debug information from other components.
- Multiple Servers at once
- Test remote connectors (http, ws)
- ...
We love contributions! Feel free to open issues for bugs or feature requests.
- Python 3.11+
- MCP implementation (like Playwright MCP)
- LangChain and appropriate model libraries (OpenAI, Anthropic, etc.)
If you use MCP-Use in your research or project, please cite:
@software{mcp_use2025,
author = {Zullo, Pietro},
title = {MCP-Use: MCP Library for Python},
year = {2025},
publisher = {GitHub},
url = {https://github.com/pietrozullo/mcp-use}
}MIT