Integrating MCP Servers to an OpenAI Agent

Welcome! Now that you’ve built your MCP server and exposed your shopping list tools, it’s time to make them available to an OpenAI agent. In this lesson, you’ll learn how to connect your MCP server to an agent using both local (stdio) and remote (SSE) transports, how to provide the server to the agent, and how to test that the integration works as expected.

By the end of this lesson, you’ll be able to:

• Connect an OpenAI agent to your MCP server using both stdio and SSE transports.
• Provide the MCP server to the agent so it can discover and use your tools.
• Run and test the integration, verifying that the agent can answer queries using your shopping list service.

Let’s walk through each step in detail.

The simplest way to connect your MCP server to an agent is by using the stdio transport. This is ideal for local development, where your server runs as a subprocess on the same machine as your agent. Communication happens over standard input and output, making it fast and easy to set up.

Here’s how you can launch your MCP server and connect to it via stdio using the OpenAI Agents SDK:

import asyncio
from agents.mcp import MCPServerStdio

async def main():
    # Define how to start your MCP server as a subprocess
    server_params = {
        "command": "python",
        "args": ["mcp_server.py"]
    }

    # Create a connection to the MCP server using stdio
    async with MCPServerStdio(params=server_params) as mcp_server:
        # Your agent code goes here...

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

• The command and args specify how to launch your MCP server script.
• The MCPServerStdio context manager handles starting and stopping the server process for you.

This setup is perfect for development and testing on your own machine.

If your MCP server is running remotely—perhaps on another machine or in the cloud—you’ll want to use the SSE (Server-Sent Events) transport. This allows the agent to communicate with your server over HTTP, making it suitable for distributed or production environments.

Here’s how to connect using SSE:

import asyncio
from agents.mcp import MCPServerSse

async def main():
    # Specify the URL where your MCP server is running
    server_params = {"url": "http://localhost:3000/sse"}

    # Create a connection to the MCP server using SSE
    async with MCPServerSse(params=server_params) as mcp_server:
        # Your agent code goes here...

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

• Replace the URL with the address of your running MCP server.
• The MCPServerSse context manager manages the HTTP connection for you.

This approach is great for connecting to servers that are not running on your local machine.