Pydantic AI

Connect Unizo MCP to Pydantic AI with built-in MCP server support.

Overview

Pydantic AI includes native MCP integration via the mcp_servers parameter, enabling seamless connection to Unizo's MCP server.

Pydantic AI Documentation

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 Pydantic AI
• OpenAI API Key: For the underlying LLM

Installation

uv add pydantic-ai python-dotenv openai

Or using pip:

pip install pydantic-ai python-dotenv openai

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 LangChain 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 LangChain 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 create an agent:

import os
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP
from dotenv import load_dotenv

load_dotenv()

# Create agent with Unizo MCP server
agent = Agent(
    model="openai:gpt-5",
    mcp_servers=[
        MCPServerStreamableHTTP(
            url="https://api.unizo.ai/mcp",
            headers={
                "servicekey": os.getenv('UNIZO_SERVICE_KEY'),
                "x-mcp-scopes": "TICKETING" # Comma-separated scopes (TICKETING, VMS, etc.)
            }
        )
    ]
)

# Query the agent
result = agent.run_sync("List available ticketing connectors from Unizo")
print(result.data)

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.