Getting Started with MCP using OpenAI Agents

python -m venv env
source env/bin/activate
pip install openai-agents weave
curl -Ls https://astral.sh/uv/install.sh | sh

git clone --depth=1 https://github.com/openai/openai-agents-python.git temp_agents && cp -R temp_agents/examples/mcp/filesystem_example/sample_files ./ && cp temp_agents/examples/mcp/filesystem_example/main.py ./ && rm -rf temp_agents

sample_files/
main.py

import asyncio
import os
import shutil

from agents import Agent, Runner, set_trace_processors
from agents.mcp import MCPServer, MCPServerStdio
from weave.integrations.openai_agents.openai_agents import WeaveTracingProcessor
import weave

# Initialize Weave project
weave.init("mcp_stuff")
set_trace_processors([WeaveTracingProcessor()])  # Enable Weave tracing

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],
    )

    message = "Read the files and list them."
    print(f"Running: {message}")
    result = await Runner.run(starting_agent=agent, input=message)
    print(result.final_output)

    message = "What is my #1 favorite book?"
    print(f"\n\nRunning: {message}")
    result = await Runner.run(starting_agent=agent, input=message)
    print(result.final_output)

    message = "Look at my favorite songs. Suggest one new song that I might like."
    print(f"\n\nRunning: {message}")
    result = await Runner.run(starting_agent=agent, input=message)
    print(result.final_output)

async def main():
    current_dir = os.path.dirname(os.path.abspath(__file__))
    samples_dir = os.path.join(current_dir, "sample_files")

    async with MCPServerStdio(
        name="Filesystem Server, via npx",
        params={
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir],
        },
    ) as server:
        await run(server)

if __name__ == "__main__":
    if not shutil.which("npx"):
        raise RuntimeError("npx is not installed. Please install it with `npm install -g npx`.")
    asyncio.run(main())

uv run python main.py

git clone https://github.com/wandb/mcp-server.git

import asyncio
import os
import shutil

from agents import Agent, Runner, set_trace_processors
from agents.mcp import MCPServerStdio
from weave.integrations.openai_agents.openai_agents import WeaveTracingProcessor
import weave

weave.init("oai_agent")
set_trace_processors([WeaveTracingProcessor()])  # Enables automatic Weave tracing

SERVER_DIR = "/Users/brettyoung/Desktop/dev_24/tutorials/oai_mcp/mcp-server/src/mcp_server" # put your full path to the weave server here 
UV_PATH = shutil.which("uv")

async def setup_agent():
    mcp_server = MCPServerStdio(
        name="Weave MCP Server (via uv)",
        params={
            "command": UV_PATH,
            "args": ["--directory", SERVER_DIR, "run", "server.py"],
        },
    )
    await mcp_server.__aenter__()
    agent = Agent(
        name="Assistant",
        instructions="Use the tools to query Weave or analyze traces to answer questions.",
        mcp_servers=[mcp_server],
    )
    return agent, mcp_server

# @weave.op
async def chat_loop(agent):
    print("\nChat with the Weave Agent (type 'exit' to quit):\n")
    while True:
        msg = input("You: ").strip()
        if msg.lower() in ["exit", "quit"]:
            break
        result = await Runner.run(starting_agent=agent, input=msg)
        print(f"Assistant: {result.final_output}\n")

async def main():
    if not UV_PATH:
        raise RuntimeError("uv not found in PATH")
    agent, mcp_server = await setup_agent()
    try:
        await chat_loop(agent)
    finally:
        await mcp_server.__aexit__(None, None, None)

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


generate a report of recent LLM activity in the byyoung3/qat project