> ## Documentation Index
> Fetch the complete documentation index at: https://ikun.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# SSE 传输

Agno 的 MCP 集成支持 [SSE 传输](https://modelcontextprotocol.io/docs/concepts/transports#server-sent-events-sse)。此传输支持服务器到客户端的流式传输，在处理受限网络时可能比 [stdio](https://modelcontextprotocol.io/docs/concepts/transports#standard-input%2Foutput-stdio) 更有用。

要使用它，请初始化 `MCPTools`，传入 MCP 服务器的 URL 并将传输设置为 `sse`：

```python theme={null}
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mcp import MCPTools

server_url = "http://localhost:8000/sse"

async with MCPTools(url=server_url, transport="sse") as mcp_tools:
    agent = Agent(model=OpenAIChat(id="gpt-4o"), tools=[mcp_tools])
    await agent.aprint_response("What is the license for this project?", stream=True)
```

您还可以使用 `server_params` 参数来定义 MCP 连接。这样，您可以指定每次请求发送到 MCP 服务器的标头以及超时值：

```python theme={null}
from agno.tools.mcp import MCPTools, SSEClientParams

server_params = SSEClientParams(
    url=...,
    headers=...,
    timeout=...,
    sse_read_timeout=...,
)

async with MCPTools(server_params=server_params) as mcp_tools:
    ...
```

## 完整示例

设置一个简单的本地服务器并使用 SSE 传输连接到它：

<Steps>
  <Step title="设置服务器">
    ```python sse_server.py theme={null}
    from mcp.server.fastmcp import FastMCP

    mcp = FastMCP("calendar_assistant")


    @mcp.tool()
    def get_events(day: str) -> str:
        return f"There are no events scheduled for {day}."


    @mcp.tool()
    def get_birthdays_this_week() -> str:
        return "It is your mom's birthday tomorrow"


    if __name__ == "__main__":
        mcp.run(transport="sse")
    ```
  </Step>

  <Step title="设置客户端">
    ```python sse_client.py theme={null}
    import asyncio

    from agno.agent import Agent
    from agno.models.openai import OpenAIChat
    from agno.tools.mcp import MCPTools, MultiMCPTools

    # 这是我们要使用的 MCP 服务器的 URL。
    server_url = "http://localhost:8000/sse"


    async def run_agent(message: str) -> None:
        async with MCPTools(transport="sse", url=server_url) as mcp_tools:
            agent = Agent(
                model=OpenAIChat(id="gpt-4o"),
                tools=[mcp_tools],
                markdown=True,
            )
            await agent.aprint_response(message=message, stream=True, markdown=True)


    # 使用 MultiMCPTools，我们可以同时连接到多个 MCP 服务器，即使它们使用不同的传输。
    # 在此示例中，我们同时连接到示例服务器（SSE 传输）和另一个服务器（stdio 传输）。
    async def run_agent_with_multimcp(message: str) -> None:
        async with MultiMCPTools(
            commands=["npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt"],
            urls=[server_url],
        ) as mcp_tools:
            agent = Agent(
                model=OpenAIChat(id="gpt-4o"),
                tools=[mcp_tools],
                markdown=True,
            )
            await agent.aprint_response(message=message, stream=True, markdown=True)


    if __name__ == "__main__":
        asyncio.run(run_agent("Do I have any birthdays this week?"))
        asyncio.run(
            run_agent_with_multimcp(
                "Can you check when is my mom's birthday, and if there are any AirBnb listings in SF for two people for that day?"
            )
        )
    ```
  </Step>

  <Step title="运行服务器">
    ```bash theme={null}
    python sse_server.py
    ```
  </Step>

  <Step title="运行客户端">
    ```bash theme={null}
    python sse_client.py
    ```
  </Step>
</Steps>