How to Use the Iterable MCP in OpenAI Agents SDK

Q: How do I register the Iterable MCP Server with OpenAI Agents SDK?

Install openai-agents and instantiate MCPServerStreamableHttp with your Vinkius endpoint. Pass this instance inside the mcpservers list when initializing your agent.

Q: Can my OpenAI Agents SDK system handle real-time webhook audits?

Yes, your agent calls listwebhooks to inspect active endpoints and data destinations. This lets you monitor integration health directly through the OpenAI tracing dashboard.

Q: How does the OpenAI Agents SDK optimize tool lookup speed for Iterable?

Set cacheToolsList=True during initialization to cache the schema of all ten tools. This avoids redundant HTTP handshakes when your agent boots up.

Q: How does Vinkius handle data privacy for my Iterable user emails?

Vinkius processes all getuser queries in ephemeral V8 isolates that destroy execution state immediately after returning the profile. Your API keys and user records never persist on disk.

Connect your production OpenAI Agents SDK pipelines to Iterable user and campaign data with built-in execution guardrails.

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Connect Iterable MCP to OpenAI Agents SDK

Create your Vinkius account to connect Iterable to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Iterable with OpenAI Agents SDK

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Audit active marketing campaigns directly from your Python runtime

`list_campaigns` pulls active marketing campaigns and their IDs without manual dashboard lookups. You get raw JSON payloads containing names, statuses, and audience targeting rules. This MCP Server lets your OpenAI agent map out your current outreach footprint before making any messaging decisions. Once your OpenAI agent identifies the correct ID, it calls `get_campaign` to inspect targeting criteria and schedules. The agent uses this structural data to ensure it doesn't cross-wire communications or target the wrong segment. OpenAI tracing tracks these tool calls in real-time, giving you an audit trail of every database lookup.

Map automated lifecycle logic inside your OpenAI Agents SDK workflow

`list_workflows` fetches the active automation triggers and message sequences running inside your marketing platform. Your agent reads this logic to understand what automated emails or push notifications a customer is already scheduled to receive. It prevents your system from double-messaging users with overlapping triggers. Your OpenAI agent cross-references these automation paths with `list_message_types` to verify the exact category of each outbound message. By analyzing these categories, the agent routes transactional messages safely while respecting user opt-out preferences.

Run deep subscriber lookups using this MCP Server

`get_user` pulls complete subscriber profiles by email, returning custom fields, list memberships, and historic event logs. Your agent inspects this profile to diagnose delivery issues or personalize dynamic content blocks. To verify which segments the user belongs to, the agent queries `list_lists` to pull static and dynamic contact groups. This setup allows your OpenAI Agents SDK to make execution decisions based on real subscriber data, bypassing stale database mirrors.

Setup guide

Set up Iterable MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Iterable tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Iterable tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Iterable tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Iterable Agent",
            instructions="You have access to Iterable tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Iterable MCP in OpenAI Agents SDK

Install `openai-agents` and instantiate `MCPServerStreamableHttp` with your Vinkius endpoint. Pass this instance inside the `mcp_servers` list when initializing your agent.

Yes, your agent calls `list_webhooks` to inspect active endpoints and data destinations. This lets you monitor integration health directly through the OpenAI tracing dashboard.

Yes. You can route campaign analysis to a reporting agent using `get_campaign_metrics` while a user agent queries user profiles via `get_user`.

Set `cacheToolsList=True` during initialization to cache the schema of all ten tools. This avoids redundant HTTP handshakes when your agent boots up.

Vinkius processes all `get_user` queries in ephemeral V8 isolates that destroy execution state immediately after returning the profile. Your API keys and user records never persist on disk.

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