Using FastMCP with OpenAI and Avoiding Session Termination Issues

Using FastMCP with OpenAI: Avoiding Session Termination Issues

When integrating FastMCP as a Model Context Protocol (MCP) server for OpenAI’s Responses API, you may encounter a frustrating issue:

OpenAI sends DELETE requests to end the MCP session after each call, without automatically creating new sessions for subsequent requests. This will result in FastMCP responding:

{
    "jsonrpc": "2.0",
    "id": "server-error",
    "error": {
        "code": -32600,
        "message": "Not Found: Session has been terminated"
    }
}

The Fix: stateless_http=True

When instantiating your FastMCP server, set:

from fastmcp import FastMCP

mcp = FastMCP(
    name="MyToolServer",
    stateless_http=True  # crucial for streaming stability
)

@mcp.tool()
def greet(name: str) -> str:
    return f"Hello, {name}!"

if __name__ == "__main__":
    mcp.run(
        transport="http",
        host="0.0.0.0",
        port=8000
    )

Why This Works

stateless_http=True disables stateful session tracking.
Every request is handled independently, so OpenAI’s DELETE calls don’t break the connection.
Without it, streaming sessions will be terminated prematurely.
More details: OpenAI Forum Thread

Caveats

• Session IDs won’t persist between requests, so advanced workflows relying on session state won’t work.
• Sampling and certain bi-directional streaming patterns may not be supported in stateless mode.

TL;DR

If your OpenAI + FastMCP streaming requests fail after one call,
set stateless_http=True when starting the server.
It’s the simplest fix to keep your MCP server ready for every new streaming request.

Comments

[Chandler]

Thanks for this tip! This is more useful to me as a debug tool as my code with errors now properly outputs error messages rather than just "Session Terminated" in the OpenAI Prompt Logs.