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The OpenAI Agents SDK supports MCP servers with multiple transport options, enabling Python agents to use MCP tools.

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Installation

pip install agents

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Streamable HTTP (Production)

Use MCPServerStreamableHttp for self-managed MCP servers: 
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    async with MCPServerStreamableHttp(
        name="GitHub Server",
        params={
            "url": "https://mcp.runlayer.com/github-a1b2c3/mcp",
            "headers": {"x-runlayer-api-key": "your-api-key"},
            "timeout": 10,
        },
        cache_tools_list=True,
    ) as server:
        agent = Agent(
            name="GitHub Assistant",
            instructions="Use GitHub tools to help with repository tasks.",
            mcp_servers=[server],
        )

        result = await Runner.run(
            agent,
            "Get info about the vercel/ai repository"
        )
        print(result.final_output)

asyncio.run(main())

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stdio Transport (Local Development)

For local Runlayer MCP servers: 
from pathlib import Path
from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    name="Local Runlayer Server",
    params={
        "command": "runlayer",
        "args": [
            "936ac3e8-bb75-428d-8db1-b1f08ff07816",
            "--secret", "your-secret-key",
            "--host", "https://mcp.runlayer.com"
        ],
    },
) as server:
    agent = Agent(
        name="Assistant",
        instructions="Help with tasks using local MCP server.",
        mcp_servers=[server],
    )
    result = await Runner.run(agent, "Use the available tools")
    print(result.final_output)

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Hosted MCP Tools

Let OpenAI’s infrastructure call MCP servers: 
from agents import Agent, Runner, HostedMCPTool

agent = Agent(
    name="Assistant",
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "github",
                "server_url": "https://mcp.runlayer.com/github-a1b2c3/mcp",
                "require_approval": "never",
            }
        )
    ],
)

result = await Runner.run(agent, "Get repository info")
print(result.final_output)

​
Tool Filtering

Filter which tools to expose: 
from agents.mcp import MCPServerStreamableHttp, create_static_tool_filter

async with MCPServerStreamableHttp(
    name="GitHub Server",
    params={"url": "https://mcp.runlayer.com/github-a1b2c3/mcp"},
    tool_filter=create_static_tool_filter(
        allowed_tool_names=["get_repository", "list_issues"]
    ),
) as server:
    agent = Agent(name="Assistant", mcp_servers=[server])
    result = await Runner.run(agent, "Get repo info")
    print(result.final_output)

​
Multiple MCP Servers

Combine tools from multiple servers: 
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    async with MCPServerStreamableHttp(
        name="GitHub",
        params={
            "url": "https://mcp.runlayer.com/github-a1b2c3/mcp",
            "headers": {"Authorization": "Bearer github-token"},
        },
    ) as github_server, MCPServerStreamableHttp(
        name="Linear",
        params={
            "url": "https://mcp.runlayer.com/linear-d4e5f6/mcp",
            "headers": {"Authorization": "Bearer linear-token"},
        },
    ) as linear_server:
        agent = Agent(
            name="Project Manager",
            instructions="Help coordinate GitHub and Linear tasks.",
            mcp_servers=[github_server, linear_server],
        )

        result = await Runner.run(
            agent,
            "Create a GitHub issue and a Linear ticket for the bug"
        )
        print(result.final_output)

asyncio.run(main())

​
Using MCP Prompts

Fetch prompts from MCP servers: 
async with MCPServerStreamableHttp(
    name="Server",
    params={"url": "https://mcp.runlayer.com/server-xyz/mcp"},
) as server:
    # Get available prompts
    prompts = await server.list_prompts()

    # Fetch a specific prompt
    prompt_result = await server.get_prompt(
        "generate_instructions",
        {"focus": "code review", "language": "python"}
    )

    instructions = prompt_result.messages[0].content.text

    agent = Agent(
        name="Code Reviewer",
        instructions=instructions,
        mcp_servers=[server],
    )

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