OpenAI Agents SDK

Integrate Unizo MCP with OpenAI Agents SDK to create sophisticated AI agents with built-in MCP server support.

Overview

OpenAI Agents SDK includes native MCP support via MCPServerStreamableHttp, enabling direct integration with Unizo's MCP server for unified access to all the categories.

Prerequisites

Before setting up the MCP integration, you'll need:

• Unizo API Key: Used for authentication (found in the Unizo Console)
• Python 3.10+: Required for OpenAI Agents SDK
• OpenAI API Key: For the underlying LLM

Installation

pip install openai-agents

Or with uv:

uv add openai-agents

Authentication

Step 1: Generate a Service Key

Use your Unizo API key to generate a service key for MCP authentication:

curl --location 'https://api.unizo.ai/api/v1/serviceKeys' \
  --header "apiKey: {unizo_api_key}" \
  --header "Content-Type: application/json" \
  --data '{
    "name": "MCP OpenAI Agents Integration",
    "subOrganization": {
      "name": "{YOUR_CUSTOMER_NAME}",
      "externalKey": "{YOUR_CUSTOMER_UNIQUE_IDENTIFIER}"
    },
    "integration": {
      "target": {
        "categorySelectors": [
          {
            "type": "TICKETING"
          }
        ]
      }
    }
  }'

Required Parameters:

Parameter	Description
apiKey	Your Unizo API key (found in the Unizo Console)
name	Descriptive name for the integration (e.g., "MCP OpenAI Agents Integration")
subOrganization.name	Your organization name (e.g., "Acme Inc")
subOrganization.externalKey	Unique identifier (UUID format recommended)

Response:

{
    "state": "ACTIVE",
    "displayId": "{UNIZO_SERVICE_KEY}",
    ...
}

Important: Save the displayId from the response this is your service key for authenticating with the MCP server.

Step 2: Configure Environment Variables

Create a .env file in your project root:

OPENAI_API_KEY=your_openai_api_key

Quick Start

Connect to Unizo MCP and use tools with OpenAI agents:

import os
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Create MCP server connection for Unizo
    async with MCPServerStreamableHttp(
        name="Unizo MCP Server",
        params={
            "url": "https://api.unizo.ai/mcp",
            "headers": {
                "servicekey": os.getenv('UNIZO_SERVICE_KEY'),
                "x-mcp-scopes": "TICKETING" # Comma-separated scopes (TICKETING, VMS, etc.)
            },
            "timeout": 10,
        },
    ) as server:
        # Create agent with Unizo MCP tools
        agent = Agent(
            name="unizo-assistant",
            instructions="Use the Unizo MCP tools to answer questions about integrations and ticketing.",
            mcp_servers=[server],
        )
        # Run agent with Unizo tools
        result = await Runner.run(agent, "List all available ticketing connectors")
        print(result.final_output)

# Run the async main function
if __name__ == "__main__":
    asyncio.run(main())

Headers

Header	Description
servicekey	Your Unizo service key (from the authentication step)
x-mcp-scopes (optional)	Supported scopes include TICKETING, VMS, INCIDENT, SCM, PCR, COMMS, IDENTITY, OBSERVABILITY, KEY_MANAGEMENT, INFRA, EDR, STORAGE, and PLATFORM. Note: If x-mcp-scopes is not specified, all scopes are enabled by default.

Advanced Configuration

Connection Options

async with MCPServerStreamableHttp(
    name="Unizo MCP Server",
    params={
        "url": "https://api.unizo.ai/mcp",
        "headers": {
            "servicekey": os.getenv('UNIZO_SERVICE_KEY'),
            "x-mcp-scopes": "TICKETING,PLATFORM"
        },
        "timeout": 30,  # Request timeout in seconds
    },
    cache_tools_list=True,      # Cache available tools for performance
    max_retry_attempts=3,       # Retry failed requests
) as server:
    # Your agent code here