Hosted MCP Server

name: hosted-mcp-server description: Creates and configures hosted Model Context Protocol (MCP) server connections for OpenAI Agents SDK

Hosted MCP Server Skill

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

Purpose

• Create HostedMCPTool configurations
• Configure server parameters and authentication
• Set up approval flows for sensitive operations
• Connect to publicly accessible MCP servers

HostedMCPTool Properties

• tool_config (Mcp object/dict): The MCP tool config, which includes the server URL and other settings
• on_approval_request (function): An optional function that will be called if approval is requested for an MCP tool. The function takes an MCPToolApprovalRequest object as input and returns a dictionary with:
  • approve (bool): Whether to approve the tool call
  • reason (string, optional): An optional reason, if rejected
  • The function can be synchronous or asynchronous (return type can be awaitable)

Mcp Object Properties (for tool_config)

• type (string, required): The type of the MCP tool. Always "mcp"
• server_label (string, required): A label for this MCP server, used to identify it in tool calls
• server_url (string): The URL for the MCP server (one of server_url or connector_id must be provided)
• server_description (string): Optional description of the MCP server, used to provide more context
• connector_id (string): Identifier for service connectors (one of server_url or connector_id must be provided)
• authorization (string): OAuth access token that can be used with a remote MCP server
• headers (dict): Optional HTTP headers to send to the MCP server (for authentication or other purposes)
• allowed_tools (McpAllowedTools): List of allowed tool names or a filter object
• require_approval (McpRequireApproval): Specify which of the MCP server's tools require approval

MCPToolApprovalRequest Properties

• ctx_wrapper (RunContextWrapper): The run context
• data (McpApprovalRequest): The data from the MCP tool approval request

Connector IDs Supported

• connector_dropbox
• connector_gmail
• connector_googlecalendar
• connector_googledrive
• connector_microsoftteams
• connector_outlookcalendar
• connector_outlookemail
• connector_sharepoint

Approval Options

• "always": Always require approval
• "never": Never require approval
• Object mapping: Specific approval policies per tool

Usage Context

Use this skill when:

• Connecting to publicly accessible MCP servers
• Using OpenAI's infrastructure for tool execution
• Working with service connectors
• Needing hosted tool execution without local callbacks

Basic Example

import asyncio

from agents import Agent, HostedMCPTool, Runner

async def main() -> None:
    agent = Agent(
        name="Assistant",
        tools=[
            HostedMCPTool(
                tool_config={
                    "type": "mcp",
                    "server_label": "gitmcp",
                    "server_url": "https://gitmcp.io/openai/codex",
                    "require_approval": "never",
                }
            )
        ],
    )

    result = await Runner.run(agent, "Which language is this repository written in?")
    print(result.final_output)

asyncio.run(main())

Streaming Example

result = Runner.run_streamed(agent, "Summarise this repository's top languages")
async for event in result.stream_events():
    if event.type == "run_item_stream_event":
        print(f"Received: {event.item}")
print(result.final_output)

Approval Flow Example

Use when servers can perform sensitive operations that require human or programmatic approval before execution. Configure require_approval with policies ("always", "never") or a dict mapping tool names to policies.

from agents import Agent, HostedMCPTool, MCPToolApprovalFunctionResult, MCPToolApprovalRequest

SAFE_TOOLS = {"read_project_metadata"}

def approve_tool(request: MCPToolApprovalRequest) -> MCPToolApprovalFunctionResult:
    if request.data.name in SAFE_TOOLS:
        return {"approve": True}
    return {"approve": False, "reason": "Escalate to a human reviewer"}

agent = Agent(
    name="Assistant",
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "gitmcp",
                "server_url": "https://gitmcp.io/openai/codex",
                "require_approval": "always",
            },
           
        )
    ],
)

Connector-Backed Example

Use for OpenAI connectors instead of specifying a server_url. The Responses API handles authentication and exposes the connector's tools.

import os

HostedMCPTool(
    tool_config={
        "type": "mcp",
        "server_label": "google_calendar",
        "connector_id": "connector_googlecalendar",
        "authorization": os.environ["GOOGLE_CALENDAR_AUTHORIZATION"],
        "require_approval": "never",
    }
)

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