How to Use the Adversus MCP in OpenAI Agents SDK

Q: Can Adversus handle lead lists in OpenAI Agents SDK?

Yes, use listactiveleads to pull your current pipeline directly into the agent's context. The agent can then sort or prioritize these leads based on your specific rules.

Use Adversus with OpenAI Agents SDK to manage outbound calls and track leads directly in your production agent pipeline.

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Connect Adversus MCP to OpenAI Agents SDK

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

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Automate contact management in OpenAI Agents SDK

Your agent can pipe new prospects into active outreach lists using `add_contact_to_campaign`. This keeps your calling workflow moving without manual input. It handles the heavy lifting of data entry while your agent keeps its focus on the next call. No more jumping between tabs to update records.

Track campaign performance with OpenAI Agents SDK

Run `get_campaign_details` or `list_campaigns` to pull real-time stats into your OpenAI Agents SDK runtime. You get clear visibility into how your outreach efforts stack up. Use this data to shift your agent's focus toward high-performing projects. It turns your agent into an active participant in your sales cycle.

Manage team access with OpenAI Agents SDK

Call `list_account_users` to see exactly who is on your Adversus team. This MCP server lets your agent verify permissions before triggering sensitive operations. It keeps your agent aware of the human team members currently handling specific accounts. You maintain control over who interacts with your lead data.

Setup guide

Set up Adversus 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 Adversus tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Adversus 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="Adversus Agent",
            instructions="You have access to Adversus 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 Adversus. 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 Adversus MCP in OpenAI Agents SDK

You initialize an MCPServerStreamableHttp instance with your endpoint token and pass it to the agent constructor. This connects the Adversus tools automatically.

Yes, use `list_active_leads` to pull your current pipeline directly into the agent's context. The agent can then sort or prioritize these leads based on your specific rules.

Vinkius handles authentication through a single endpoint token. Your data stays within the secure sandbox environment provided by the server.

The Python implementation is built for async usage. You should use a context manager to ensure connections close properly after your agent finishes its task.

The agent receives a standard error response. You can configure your agent's logic to retry or alert you if the Adversus service is unreachable.

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