quickstart.mdx

For the complete documentation index, see [llms.txt](https://mcp-use.com/docs/llms.txt).

mcp-use is a unified MCP framework to build MCP servers, MCP clients and MCP Agents.

%%{init: {'theme':'neutral'}}%%
graph LR
    Agent[MCP Agent] -->|uses| Client[MCP Client]
    Client -->|connects to| Server[MCP Server]
    Server -->|provides tools| Client

Section	Description
Agent	An AI Agent with tool calling capabilities through MCP connections	Examples
Client	A full featured MCP Client implementation for Python with async support	Examples
Server	The MCP Server framework implementation for Python	Examples

MCP Agent

mcp-use has a complete MCP Agent implementation for Python. MCP Agents are AI-powered agents that can use tools from MCP servers to accomplish complex tasks. They reason across multiple steps, selecting and executing tools as needed. Building such agents is easy with mcp-use, all you need is an LLM and the MCP Client.

```bash pip pip install mcp-use langchain-openai ``` ```bash uv uv add mcp-use langchain-openai ``` ```bash poetry poetry add mcp-use langchain-openai ``` The agent requires a LangChain LLM provider package. The examples use `langchain-openai` - swap for `langchain-anthropic`, `langchain-google-genai`, or `langchain-groq` as needed.

Here's a simple example to get you started for an agent with browser tools support.

import asyncio
from langchain_openai import ChatOpenAI  # use your preferred LLM provider
from mcp_use import MCPAgent, MCPClient

async def main():
    # Create MCPClient from configuration object
    client = MCPClient({
        "mcpServers": {
            "playwright": {
                "command": "npx",
                "args": ["@playwright/mcp@latest"],
                "env": {
                    "DISPLAY": ":1"
                }
            }
        }
    })

    # Create agent with the client
    agent = MCPAgent(
        llm=ChatOpenAI(model="gpt-5.5"),  # use your preferred LLM provider
        client=client,
        max_steps=30
    )

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

    # Clean up
    await client.close_all_sessions()

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

For multi-server setups, tool restrictions, and advanced configuration options, see the [Agent Configuration](/python/agent/agent-configuration) and [Client Configuration](/python/client/client-configuration) guides.

For a complete overview of the mcp-use MCP Agent, see the MCP Agent documentation.

Available MCP Servers

mcp-use supports any MCP server. Check out the Awesome MCP Servers list for available options. Or deploy your own following the instruction below.

MCP Client

mcp-use has a complete MCP Client implementation for Python with full async support. It supports stdio and HTTP with SSE transports for connecting to any MCP server.

```bash pip pip install mcp-use ``` ```bash uv uv add mcp-use ``` ```bash poetry poetry add mcp-use ```

Then create a new MCP Client:

import asyncio
from mcp_use import MCPClient

async def main():
    client = MCPClient({
        "mcpServers": {
            "everything": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-everything"]
            }
        }
    })

    # Initialize all configured sessions
    await client.create_all_sessions()

    # Get the session for a specific server
    session = client.get_session("everything")

    # List available tools
    tools = await session.list_tools()
    print(f"Available tools: {[t.name for t in tools]}")

    # Call a specific tool with arguments
    result = await session.call_tool("add", {"a": 1, "b": 2})
    print(f"Result: {result}")

    # Clean up
    await client.close_all_sessions()

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

To learn more about the MCP Client, see the MCP Client documentation.

MCP Server

mcp-use has a complete MCP server framework implementation for Python. It supports all official MCP features including tools, resources, prompts, and middleware.

```bash pip pip install mcp-use ``` ```bash uv uv add mcp-use ``` ```bash poetry poetry add mcp-use ```

Scaffold a new project

# Via uvx (from PyPI)
uvx --from mcp-use create-mcp-use my-server

# Or if mcp-use is installed locally
create-mcp-use my-server

# Start the server
cd my-server
python server.py

Create an MCP server manually:

from mcp_use import MCPServer

server = MCPServer(
    name="my-server",
    version="1.0.0",
    instructions="A simple example server"
)

# Define a tool
@server.tool()
def get_weather(city: str) -> dict:
    """Get weather for a city"""
    return {"temperature": 72, "condition": "sunny", "city": city}

# Start the server
if __name__ == "__main__":
    server.run()

To learn more about the MCP Server, see the MCP Server documentation.

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Next Steps

Configure your Agent and connected MCP servers Explore supported LLM providers and their setup Browse real-world examples and use cases **Need Help?** Join our [Discord](https://discord.gg/XkNkSkMz3V) or [Github](https://github.com/mcp-use/mcp-use) communities.