Run any Skill in Manus with one click

Get Started

streamable-http-mcp-server

Stars0

Forks0

UpdatedFebruary 4, 2026 at 04:59

Creates and configures Streamable HTTP Model Context Protocol (MCP) server connections for OpenAI Agents SDK

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Run Skill in Manus

Source

syeda-hoorain-ali

syeda-hoorain-ali/todo-spec-driven-hackathon

View GitHub Repository View Creator Repositories

Download

Run Skill in Manus

Related occupationsSOC

Based on SOC occupation classification

Software DevelopersComputer and Mathematical Occupations·SOC 15-1252

SKILL.md

readonly

name	streamable-http-mcp-server
description	Creates and configures Streamable HTTP Model Context Protocol (MCP) server connections for OpenAI Agents SDK

Streamable HTTP MCP Server Skill

This skill helps create and configure Streamable HTTP Model Context Protocol (MCP) server connections for OpenAI Agents SDK.

Purpose

Create MCPServerStreamableHttp configurations
Configure HTTP connection parameters and authentication
Set up caching and retry mechanisms
Connect to HTTP-based MCP servers with direct connection management

MCPServerStreamableHttp Constructor Parameters

params (MCPServerStreamableHttpParams): Connection parameters for the server
- url (str): The URL of the server
- headers (dict[str, str], optional): The headers to send to the server
- timeout (timedelta | float, optional): The timeout for the HTTP request (default: 5 seconds)
- sse_read_timeout (timedelta | float, optional): The timeout for the SSE connection (default: 5 minutes)
- terminate_on_close (bool, optional): Whether to terminate on close
- httpx_client_factory (HttpClientFactory, optional): Custom HTTP client factory for configuring httpx.AsyncClient behavior
cache_tools_list (bool): Whether to cache the list of available tools (default: False)
name (string | None): A readable name for the server (default: None, auto-generated from URL)
client_session_timeout_seconds (float | None): Read timeout for the MCP ClientSession (default: 5)
tool_filter (ToolFilter): The tool filter to use for filtering tools (default: None)
use_structured_content (bool): Whether to use tool_result.structured_content when calling an MCP tool (default: False)
max_retry_attempts (int): Number of times to retry failed list_tools/call_tool calls (default: 0)
retry_backoff_seconds_base (float): The base delay, in seconds, for exponential backoff between retries (default: 1.0)
message_handler (MessageHandlerFnT | None): Optional handler invoked for session messages (default: None)

Usage Context

Use this skill when:

Managing HTTP connections yourself
Running servers locally or remotely with direct connection management
Needing to keep latency low with your own infrastructure
Wanting to run the server inside your own infrastructure

Basic Example

import asyncio
import os

from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
from agents.model_settings import ModelSettings

async def main() -> None:
    token = os.environ["MCP_SERVER_TOKEN"]
    async with MCPServerStreamableHttp(
        name="Streamable HTTP Python Server",
        params={
            "url": "http://localhost:8000/mcp",
            "headers": {"Authorization": f"Bearer {token}"},
            "timeout": 10,
        },
        cache_tools_list=True,
        max_retry_attempts=3,
    ) as server:
        agent = Agent(
            name="Assistant",
            instructions="Use the MCP tools to answer the questions.",
            mcp_servers=[server],
            model_settings=ModelSettings(tool_choice="required"),
        )

        result = await Runner.run(agent, "Add 7 and 22.")
        print(result.final_output)

asyncio.run(main())

More from this repository

same repository

better-auth-implementation-for-nextjs-fastapi

syeda-hoorain-ali/todo-spec-driven-hackathon

This skill provides guidance for implementing secure authentication using Better Auth for Next.js frontend and JWT verification for FastAPI backend.

2026-02-040

chatkit-client-widget-creator

syeda-hoorain-ali/todo-spec-driven-hackathon

This skill helps create React chat widgets using the ChatKit React library, following best practices for floating chat interfaces with thread persistence and proper configuration.

2026-02-040

chatkit-server-creator

syeda-hoorain-ali/todo-spec-driven-hackathon

This skill helps create ChatKit server implementations that integrate with databases and openai-agents, following best practices for conversation management and message persistence.

2026-02-040

chatkit-server-integration

syeda-hoorain-ali/todo-spec-driven-hackathon

This skill provides functionality to properly expose a ChatKit server via FastAPI routes. It handles the integration between FastAPI request/response cycle and the ChatKit server, with proper request processing and response handling.

2026-02-040

chatkit-store-creator

syeda-hoorain-ali/todo-spec-driven-hackathon

This skill helps create ChatKit store implementations with database integration, following best practices and avoiding common errors we've solved in previous implementations.

2026-02-040

dapr

syeda-hoorain-ali/todo-spec-driven-hackathon

Comprehensive DAPR (Distributed Application Runtime) skill for building distributed microservices from hello world to professional production systems. Provides guidance on DAPR setup, building blocks (service invocation, state management, pub/sub, actors, secrets), sidecar configuration, component configuration, and Kubernetes integration for scalable distributed applications.

2026-02-040

Streamable HTTP MCP Server Skill

This skill helps create and configure Streamable HTTP Model Context Protocol (MCP) server connections for OpenAI Agents SDK.

Purpose

Create MCPServerStreamableHttp configurations

Configure HTTP connection parameters and authentication

Set up caching and retry mechanisms

Connect to HTTP-based MCP servers with direct connection management

MCPServerStreamableHttp Constructor Parameters

params (MCPServerStreamableHttpParams): Connection parameters for the server

url (str): The URL of the server
headers (dict[str, str], optional): The headers to send to the server
timeout (timedelta | float, optional): The timeout for the HTTP request (default: 5 seconds)
sse_read_timeout (timedelta | float, optional): The timeout for the SSE connection (default: 5 minutes)
terminate_on_close (bool, optional): Whether to terminate on close
httpx_client_factory (HttpClientFactory, optional): Custom HTTP client factory for configuring httpx.AsyncClient behavior

cache_tools_list (bool): Whether to cache the list of available tools (default: False)

name (string | None): A readable name for the server (default: None, auto-generated from URL)

client_session_timeout_seconds (float | None): Read timeout for the MCP ClientSession (default: 5)

tool_filter (ToolFilter): The tool filter to use for filtering tools (default: None)

use_structured_content (bool): Whether to use tool_result.structured_content when calling an MCP tool (default: False)

max_retry_attempts (int): Number of times to retry failed list_tools/call_tool calls (default: 0)

retry_backoff_seconds_base (float): The base delay, in seconds, for exponential backoff between retries (default: 1.0)

message_handler (MessageHandlerFnT | None): Optional handler invoked for session messages (default: None)

Usage Context

Use this skill when:

Managing HTTP connections yourself

Running servers locally or remotely with direct connection management

Needing to keep latency low with your own infrastructure

Wanting to run the server inside your own infrastructure

Basic Example

import asyncio import os from agents import Agent, Runner from agents.mcp import MCPServerStreamableHttp from agents.model_settings import ModelSettings async def main() -> None: token = os.environ["MCP_SERVER_TOKEN"] async with MCPServerStreamableHttp( name="Streamable HTTP Python Server", params={ "url": "http://localhost:8000/mcp", "headers": {"Authorization": f"Bearer {token}"}, "timeout": 10, }, cache_tools_list=True, max_retry_attempts=3, ) as server: agent = Agent( name="Assistant", instructions="Use the MCP tools to answer the questions.", mcp_servers=[server], model_settings=ModelSettings(tool_choice="required"), ) result = await Runner.run(agent, "Add 7 and 22.") print(result.final_output) asyncio.run(main())