How to Use the AdButler MCP in OpenAI Agents SDK

Q: How do I connect AdButler to OpenAI Agents SDK?

Install the package via pip. Create an MCPServerStreamableHttp instance with your Vinkius endpoint token, then pass it to the Agent constructor using the mcpservers array.

Q: Can OpenAI Agents SDK read AdButler campaign data?

Yes. The agent uses the listcampaigns tool to read active and pending self-serve campaigns. You get raw data ready for downstream processing.

Q: Does the AdButler MCP Server support custom reporting dates?

It does. You pass specific date ranges to getperformancereport. The agent receives aggregated impressions, clicks, and CTR for those exact days.

Q: How do I map publishers to zones using AdButler and OpenAI Agents SDK?

Start by fetching the network list with listpublishers. Feed the resulting Publisher IDs directly into listzones so the agent handles the sequential fetching automatically.

Connect AdButler to OpenAI Agents SDK to automate ad reporting and campaign management in production.

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Connect AdButler MCP to OpenAI Agents SDK

Create your Vinkius account to connect AdButler 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 AdButler with OpenAI Agents SDK

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AdButler MCP Server Reporting

Fetching metrics demands the `get_performance_report` tool to pull impressions, clicks, and CTR directly into your agent. Production environments require hard facts, not hallucinated numbers. OpenAI Agents SDK adds safety constraints before executing these queries. You define the guardrails, and the system pulls exact ad data without risking unauthorized access.

Campaign validation

Checking active ads requires the `list_campaigns` and `list_creatives` tools to read self-serve advertising records and asset libraries. Your agent gets immediate access to banners and videos stored in the account. Handoffs between specialized agents work perfectly here. One agent audits the creatives, while another verifies the campaign status, all tracked through the OpenAI dashboard via this MCP integration.

Network inventory auditing

Mapping your ad network relies on the `list_publishers` and `list_zones` tools to connect inventory managers with specific placements. You pass a Publisher ID, and the agent returns every linked ad zone. Caching the tool list via `cacheToolsList=True` keeps these network queries fast. Your deployed product handles high-volume inventory checks without stalling.

Setup guide

Set up AdButler 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 AdButler tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate AdButler 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="AdButler Agent",
            instructions="You have access to AdButler tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AdButler. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AdButler MCP in OpenAI Agents SDK

Install the package via pip. Create an `MCPServerStreamableHttp` instance with your Vinkius endpoint token, then pass it to the Agent constructor using the `mcp_servers` array.

Yes. The agent uses the `list_campaigns` tool to read active and pending self-serve campaigns. You get raw data ready for downstream processing.

It does. You pass specific date ranges to `get_performance_report`. The agent receives aggregated impressions, clicks, and CTR for those exact days.

Start by fetching the network list with `list_publishers`. Feed the resulting Publisher IDs directly into `list_zones` so the agent handles the sequential fetching automatically.

Your impressions, clicks, and CTR metrics stay protected inside the Vinkius V8 Isolate Sandbox. Every execution runs in an ephemeral environment that destroys itself after the run completes. We handle the authentication via a single endpoint token so you never expose raw API keys.

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