--- title: "Tool Tuesday #6: MCP + OpenAI Agent SDK = Better AI Agents / MCP plus OpenAI Agent SDK equals Better AI Agents " newsletter: "MLOps Community" date: 2025-04-22 source: https://aaif.live/newsletters/mlopscommunity/2025-04-22-tool-tuesday-6-mcp-openai-agent-sdk-better-ai-agents-mcp-plu --- # Tool Tuesday #6: MCP + OpenAI Agent SDK = Better AI Agents / MCP plus OpenAI Agent SDK equals Better AI Agents *MLOps Community — Agentic AI Foundation, 2025-04-22* Huge thanks to OpenAI [https://go.mlops.community/f886ar] for keeping this [https://go.mlops.community/HallucinateTShirt] a design classic. ## 💡 What is MCPServerStdio? MCP + OPENAI AGENT SDK = BETTER AI AGENTS / MCP PLUS OPENAI AGENT SDK EQUALS BETTER AI AGENTS 🤖 If you are building AI agents there are different ways you can go about building it you could agent toolkits such as Langgraph [https://www.langchain.com/langgraph], smolagents [https://huggingface.co/docs/smolagents/index], CrewAI [https://www.crewai.com/], Autogen [https://microsoft.github.io/autogen/stable//index.html], Google ADK [https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/], and PydanticAI [https://ai.pydantic.dev/]. Out of all these options, one tool has risen to be the most beloved among AI builders and that is Anthropic’s Model Context Protocol (MCP [https://neurlcreators.substack.com/i/159944232/what-is-mcp]). This week’s Tool Tuesday dives into how we can build MCP-powered agents by utilizing the OpenAI Agent SDK as our agent toolkit. Throughout, you will learn the following: * How to connect an agent to a local MCP server using the MCPServerStdio * How to connect an agent to a remote MCP server using the MCPServerSse * How to build voice agents with MCP and OpenAI Agent SDK With that, let’s get started 🚀 🛠️ BUILDING LOCAL AGENTS WITH MCPSERVERSTDIO The OpenAI Agent SDK introduced support for MCP in version 0.0.7 [https://github.com/openai/openai-agents-python/releases/tag/v0.0.7], and with it came the MCPServerStdio class. This class enables the Agent SDK to connect to local MCP servers [https://neurlcreators.substack.com/i/159944232/local-communication-stdinstdout], allowing agents to interact with local tools like file systems, databases, or other services. 📺 MCP Architecture Visualization Snippet [https://youtu.be/LmCDPs7SJfQ] 💡 What is MCPServerStdio? MCPServerStdio is used inside an asynchronous context manager. It takes in the command-line arguments required to launch an MCP-compatible server locally. Here's the basic structure: Python async with MCPServerStdio( params={ "command": "cmd", "args": ["arg1", "arg2"], } ) as server: pass This context manager yields an instance of the running MCP server, which you can assign to a variable (in this case, server) and use within the context block. The params dictionary mimics a terminal command but breaks it down into: * "command": The command you would normally run (e.g. python, npx). * "args": A list of arguments you'd pass to that command. So, for the terminal command: Unset python main.py The equivalent params would be: Unset { "command": "python", "args": ["main.py"], } 🧠 Creating an Agent that Uses the MCP Server Once we’ve set up the MCP server, we can define an agent that uses it: Python agent = Agent( name="Assistant", instructions="You are a friendly AI assistant", # tools = [tool_1, tool_2] mcp_servers=[server], ) In a typical Agent SDK setup, you pass tools to the agent via the tools argument. But when using MCP, you use the mcp_servers argument instead. This allows your agent to interface with one or more MCP servers. Let’s put both parts together: Python async with MCPServerStdio( params={ "command": "cmd", "args": ["arg1", "arg2"], } ) as server: agent = Agent( name="Assistant", instructions="You are a friendly AI assistant", mcp_servers=[server], ) Now the agent has access to the MCP server and can use it like any other tool. 🗂️ FileSystem AI Agent with MCP Let’s build a simple AI agent that interacts with the local filesystem, able to browse directories, read/write files, and more. To do this, we’ll use the MCP filesystem server [https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem], which is already available as an npm package. 🧱 BOILERPLATE SETUP Here’s the basic scaffold for our program: Python import asyncio import os from agents import Agent, Runner from agents.mcp import MCPServer, MCPServerStdio async def run(mcp_server: MCPServer): pass async def main(): pass if __name__ == "__main__": asyncio.run(main()) We define two functions: * run: Contains the agent logic and runs the agent with the provided MCP server. * main: The entry point of the script. It will initialize the MCP server and pass it into run(). 🔌 STARTING THE FILESYSTEM MCP SERVER To launch the MCP filesystem server, we'll use npx: Unset npx -y @modelcontextprotocol/server-filesystem sample_folder The final argument is the folder the agent is allowed to access. For safety and clarity, restrict access to a specific folder. Here's the updated main function: Python async def main(): current_dir = os.path.dirname(os.path.abspath(__file__)) samples_dir = os.path.join(current_dir, "sample_folder") async with MCPServerStdio( name="Filesystem Server, via npx", params={ "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], }, ) as server: await run(server) 🤖 IMPLEMENTING THE RUN() FUNCTION Python async def run(mcp_server: MCPServer): agent = Agent( name="Assistant", instructions="Use the tools to read the filesystem and answer questions based on those files.", mcp_servers=[mcp_server], ) input_history = [] while True: user_input = input("\nProvide your prompt (or type 'exit' to quit): ") if user_input.lower() == "exit": print("Exiting chat.") break input_history.append( { "role": "user", "content": user_input } ) result = await Runner.run(starting_agent=agent, input=input_history) print("Assistant:", result.final_output) input_history = result.to_input_list() We maintain a chat history and continuously prompt the user for input in a REPL loop. The Runner.run() method feeds the entire conversation to the agent and returns a result we can print to the terminal. ▶️ RUNNING THE PROGRAM 1. Ensure you have npx installed (comes with Node.js). 2. Create a sample_folder in the same directory as your script: Unset mkdir sample_folder 3. Run your Python script, and start chatting with your file-aware AI assistant! You can find the complete source code in the GitHub repository [https://github.com/Neurl-LLC/OpenAI_Agent_SDK_plus_MCP/tree/main/mcp_server_stdio]. ⚡Building remote Agents with MCPServerSse Now that we've seen how to connect an agent to a local MCP server using MCPServerStdio, let’s explore how to connect to a remote MCP server [https://neurlcreators.substack.com/i/159944232/remote-communication-http-sse] using MCPServerSse. 💡 WHAT IS MCPSERVERSSE? MCPServerSse is similar to its standard output counterpart, but instead of launching a subprocess locally with command-line parameters, it connects to a remote MCP server via Server-Sent Events (SSE). This makes it ideal for distributed or microservice-based agent architectures. Here's a simple example of how to connect to a remote MCP server hosted on localhost: Python async with MCPServerSse( name="SSE Python Server", params={ "url": "http://localhost:8000/sse", }, ) as server: pass ⚙️ UNDERSTANDING THE PARAMETERS url: The HTTP endpoint where the MCP server exposes its /sse stream. This is where the SDK establishes a persistent connection to receive data from the server. 🛠️ Building an MCP Remote Server The MCPServerSse acts as a client, so we need to build a server that speaks the MCP protocol via SSE. You can do this easily using the FastMCP class provided by the MCP SDK. FastMCP offers a convenient method .sse_app() that transforms the MCP server into a Starlette [https://www.starlette.io/] ASGI app. You can then serve it using any ASGI server [https://asgi.readthedocs.io/en/latest/], such as Uvicorn. Here’s a minimal example: Python from starlette.applications import Starlette from starlette.routing import Mount from mcp.server.fastmcp import FastMCP import uvicorn # Create an MCP server mcp = FastMCP("Tutorial") # Mount the SSE server to the existing ASGI server app = Starlette( routes=[ Mount('/', app=mcp.sse_app()), ] ) if __name__ == '__main__': uvicorn.run('server:app', port=8000) This exposes two key endpoints: * /sse – for streaming events to the Agent SDK. * /messages/ – a POST endpoint the SDK uses to send messages. 🧰 Adding a Tool to the Server You can register tools on your server using the @mcp.tool() decorator. Let’s enhance our server with a utility that returns the current time in a given timezone: Python from starlette.applications import Starlette from starlette.routing import Mount from mcp.server.fastmcp import FastMCP from datetime import datetime from zoneinfo import ZoneInfo import uvicorn # Create an MCP server mcp = FastMCP("Tutorial") # Define tools @mcp.tool() def get_time_in_timezone(timezone: str) -> str: """ Given a timezone this tool returns the current time in that time zone """ try: tz = ZoneInfo(timezone) current_time = datetime.now(tz) return current_time.strftime("%Y-%m-%d %H:%M:%S %Z%z") except ValueError: return "Invalid time zone. Please provide a valid time zone name." # Mount the SSE server to the existing ASGI server app = Starlette( routes=[ Mount('/', app=mcp.sse_app()), ] ) if __name__ == '__main__': uvicorn.run('server:app', port=8000) Now when the agent queries this tool, the server will return the appropriate response. 🧠 Building the Agent Your agent code stays almost the same—just switch to using MCPServerSse instead of MCPServerStdio. Python import asyncio from agents import Agent, Runner from agents.mcp import MCPServer, MCPServerSse async def run(mcp_server: MCPServer): agent = Agent( name="Assistant", instructions="Use the tools to read the filesystem and answer questions based on those files.", mcp_servers=[mcp_server], ) input_history = [] while True: user_input = input("\nProvide your prompt (or type 'exit' to quit):") if user_input.lower() == "exit": print("Exiting chat.") break input_history.append( { "role": "user", "content": user_input } ) result = await Runner.run(starting_agent=agent, input=input_history) print("Assistant:", result.final_output) input_history = result.to_input_list() async def main(): async with MCPServerSse( name="SSE Python Server", params={ "url": "http://localhost:8000/sse", }, ) as server: await run(server) if __name__ == "__main__": asyncio.run(main()) Make sure your server.py is running before launching the agent script (main.py). You can find the complete source code in the GitHub repository [https://github.com/Neurl-LLC/OpenAI_Agent_SDK_plus_MCP/tree/main/mcp_server_sse]. 🗣️ Combining Voice Agents with MCP OpenAI added support for Voice Agents in version 0.0.6 [https://github.com/openai/openai-agents-python/releases/tag/v0.0.6] of the SDK. This exciting addition allows agents to communicate with humans using natural speech. In this section, we’ll walk you through how to combine Voice Agents with MCP to unlock powerful workflows that can interact with files, APIs, and more using voice. This guide focuses only on integrating MCP into a Voice Agent. If you’re new to Voice Agents, check out our guide: Building Voice Agents With OpenAI Agent SDK [https://neurlcreators.substack.com/p/building-voice-agents-with-openai] for a full walkthrough. 🧠 VOICE AGENT ANATOMY: WORKFLOW + PIPELINE A Voice Agent in the Agent SDK has two main components: * Workflow: Handles the core logic, like agent behavior. * Pipeline: Connects the workflow with a Text-to-Speech (TTS) model and a Speech-to-Text (STT) model, forming the full voice interaction loop. Our MCP integration will live inside the workflow. 🧱 BUILDING THE WORKFLOW WITH MCP A standard text-based agent usually includes a run() method to process the agent’s interaction. In a voice-enabled agent with MCP, we extend this to also include create() and cleanup() methods. Here's what our VoiceAgentWorkflow looks like: Python class VoiceAgentWorkflow(VoiceWorkflowBase): def __init__(self, agent: Agent, on_start, server: MCPServerStdio): self._input_history: list[TResponseInputItem] = [] self._current_agent = agent self._on_start = on_start self._server = server async def run(self, transcription: str): pass @classmethod async def create(cls, on_start): current_dir = os.path.dirname(os.path.abspath(__file__)) samples_dir = os.path.join(current_dir, "sample_folder") server = MCPServerStdio( name="Filesystem Server, via npx", params={ "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], }, ) await server.connect() assistant_agent = Agent( name="Assistant", instructions=prompt_with_handoff_instructions(""" You're speaking to a human, so be polite and concise. If the user speaks in Spanish, handoff to the Spanish agent. """), mcp_servers=[server], ) return cls(agent=assistant_agent, on_start=on_start, server=server) async def cleanup(self): await self._server.cleanup() 🛠 CREATE() METHOD This is a class method that builds your workflow and connects the MCP server using MCPServerStdio. Unlike other examples, we avoid using a context manager (async with) so that we can maintain the connection throughout the lifetime of the app. 🧹 CLEANUP() METHOD This ensures that the MCP server is gracefully cleaned up after the app exits. Here’s a link to the full workflow code [https://github.com/Neurl-LLC/OpenAI_Agent_SDK_plus_MCP/blob/main/mcp_voice_agent/workflow.py]. 🎛️ BUILDING THE VOICE AGENT PIPELINE We’ll also need to implement a create method for the pipeline so it can instantiate the asynchronous workflow. Here's how: Python class RealtimeCLIApp: def __init__(self, workflow: VoiceAgentWorkflow): self.should_send_audio = asyncio.Event() self.pipeline = VoicePipeline( workflow=workflow, stt_model="gpt-4o-transcribe", tts_model="gpt-4o-mini-tts", ) self.audio_input = StreamedAudioInput() self.audio_player = sd.OutputStream( samplerate=SAMPLE_RATE, channels=CHANNELS, dtype=FORMAT ) @classmethod async def create(cls) -> "RealtimeCLIApp": workflow = await VoiceAgentWorkflow.create(on_start=cls._on_transcription_static) return cls(workflow) @staticmethod def _on_transcription_static(transcription: str): print(f'Transcription: {transcription}') async def start_voice_pipeline(self): pass async def send_mic_audio(self): pass async def run(self): pass async def main(): app = await RealtimeCLIApp.create() try: await app.run() finally: await app.pipeline.workflow.cleanup() if __name__ == "__main__": asyncio.run(main()) We call cleanup() at the end of main() to properly shut down the MCP server. Here’s a link to the full pipeline code [https://github.com/Neurl-LLC/OpenAI_Agent_SDK_plus_MCP/blob/main/mcp_voice_agent/main.py]. 🚀 RUNNING THE VOICE AGENT To run everything smoothly, install the required dependencies: Unset pip install 'openai-agents[voice]' pip install sounddevice Now, place: * the workflow code in workflow.py * the pipeline in main.py Run the app with: Unset python main.py You’ll see a prompt in the terminal: Unset 🎤 Press 'K' to start/stop recording, 'Q' to quit. 🏁 Conclusion The combination of MCP and the OpenAI Agent SDK unlocks a whole new world of possibilities for building powerful, tool-using agents. By tapping into the growing ecosystem of MCP servers, your agents can seamlessly interact with real-world tools and resources. The OpenAI Agent SDK makes it incredibly simple to build these agents, handling the underlying complexities of memory, workflows, and context management so you can focus on what your agent should do, not how it works. Take the time to explore using MCP plus Agent SDK so you can build better agents. 🚀 That's it for Tool Tuesday! Tag us in your voice agent projects or the #agents [https://mlops-community.slack.com/archives/C0861BQ65A7] channel—we’d love to see what you build. 🛠️🤖 Start building today! Want the full step-by-step tutorial? Stephen is publishing the full guide on The Neural Blueprint [https://neurlcreators.substack.com/] by Thursday. ## HERE TO HELP Before you go, here are three ways I can help - just hit reply: * Curated intros to other community members * What problems are you dealing with? Let me help you find the best solutions through my network * Looking to augment your staff for an MLOps or AI project? I got you covered Thanks for reading, catch you next time! Interested in partnering with us? Get in touch: partners@mlops.community Thanks for reading. See you in Slack [https://go.mlops.community/slack], YouTube [https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ?view_as=subscriber], and podcast [https://home.mlops.community/public/content/] land. Oh yeah, and we are also on X [https://twitter.com/mlopscommunity] and LinkedIn [https://go.mlops.community/linkedin]. The MLOps Community newsletter is edited by Jessica Rudd [https://www.linkedin.com/in/jmrudd/]. Shoutout to Ranuga Disansa [https://go.mlops.community/i83caq] for his contributions. --- Source: https://aaif.live/newsletters/mlopscommunity/2025-04-22-tool-tuesday-6-mcp-openai-agent-sdk-better-ai-agents-mcp-plu