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

# Streamable HTTP Transport

The new [Streamable HTTP transport](https://modelcontextprotocol.io/specification/draft/basic/transports#streamable-http) replaces the HTTP+SSE transport from protocol version `2024-11-05`.

This transport enables the MCP server to handle multiple client connections, and can also use SSE for server-to-client streaming.

To use it, initialize the `MCPTools` passing the URL of the MCP server and setting the transport to `streamable-http`:

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

# Initialize and connect to the Streamable HTTP MCP server
mcp_tools = MCPTools(url="https://docs.agno.com/mcp", transport="streamable-http")
await mcp_tools.connect()

try:
    agent = Agent(model=OpenAIResponses(id="gpt-5.2"), tools=[mcp_tools])
    await agent.aprint_response("What can you tell me about MCP support in Agno?", stream=True)
finally:
    # Always close the connection when done
    await mcp_tools.close()
```

You can also use the `server_params` argument to define the MCP connection. This way you can specify the headers to send to the MCP server with every request, and the timeout values:

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

server_params = StreamableHTTPClientParams(
    url=...,
    headers=...,
    timeout=...,
    sse_read_timeout=...,
    terminate_on_close=...,
)

# Initialize and connect using server parameters
mcp_tools = MCPTools(server_params=server_params, transport="streamable-http")
await mcp_tools.connect()

try:
    # Use mcp_tools with your agent
    pass
finally:
    await mcp_tools.close()
```

## Complete example

Let's set up a simple local server and connect to it using the Streamable HTTP transport:

<Steps>
  <Step title="Setup the server">
    ```python streamable_http_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="streamable-http")
    ```
  </Step>

  <Step title="Setup the client">
    ```python streamable_http_client.py theme={null}
    import asyncio

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

    # This is the URL of the MCP server we want to use.
    server_url = "http://localhost:8000/mcp"


    async def run_agent(message: str) -> None:
        # Initialize and connect to the Streamable HTTP MCP server
        mcp_tools = MCPTools(transport="streamable-http", url=server_url)
        await mcp_tools.connect()

        try:
            agent = Agent(
                model=OpenAIResponses(id="gpt-5.2"),
                tools=[mcp_tools],
                markdown=True,
            )
            await agent.aprint_response(message=message, stream=True, markdown=True)
        finally:
            await mcp_tools.close()


    # Using MultiMCPTools, we can connect to multiple MCP servers at once, even if they use different transports.
    # In this example we connect to both our example server (Streamable HTTP transport), and a different server (stdio transport).
    async def run_agent_with_multimcp(message: str) -> None:
        # Initialize and connect to multiple MCP servers with different transports
        mcp_tools = MultiMCPTools(
            commands=["npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt"],
            urls=[server_url],
            urls_transports=["streamable-http"],
        )
        await mcp_tools.connect()

        try:
            agent = Agent(
                model=OpenAIResponses(id="gpt-5.2"),
                tools=[mcp_tools],
                markdown=True,
            )
            await agent.aprint_response(message=message, stream=True, markdown=True)
        finally:
            await mcp_tools.close()


    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="Run the server">
    ```bash theme={null}
    python streamable_http_server.py
    ```
  </Step>

  <Step title="Run the client">
    ```bash theme={null}
    python streamable_http_client.py
    ```
  </Step>
</Steps>