How to Use the ChatGen MCP in OpenAI Agents SDK

Q: Is the ChatGen MCP Server compatible with OpenAI Agents SDK tracing?

Yes, every tool call is traced directly. You can monitor listconversations and getleaddetails execution times directly in your OpenAI developer dashboard. This gives you complete visibility into agent latency and tool usage.

Q: How do I pass ChatGen tools to my OpenAI Agents SDK agent?

You register the server using MCPServerStreamableHttp with your connection URL. Pass this instance inside the mcpservers list when initializing your agent. The SDK handles tool discovery automatically at runtime.

Q: Can I use ChatGen with OpenAI Agents SDK for lead qualification?

Yes, you can build an agent that runs listleads and checks profiles using getleaddetails. The agent can then trigger actions based on conversation history fetched via listconversations.

Control your ChatGen bots and qualify leads directly inside your OpenAI Agents SDK pipeline.

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Connect ChatGen MCP to OpenAI Agents SDK

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

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Deploy ChatGen bots via OpenAI Agents SDK MCP Server

The `create_bot` tool lets your OpenAI Agents SDK system spin up custom chatbots on the fly based on runtime user signals. If an agent detects a high-value visitor segment, it instantly configures a target bot using `update_bot` to capture their email. You get full tracing on the OpenAI dashboard for every bot creation or modification. The SDK manages these MCP tools as native functions, letting your agent coordinate setup without manual API plumbing.

Qualify leads using autonomous agent guardrails

The `get_lead_details` tool pulls specific prospect profiles into your OpenAI Agents SDK execution context to trigger downstream routing. Your agent checks `list_leads` to find fresh contacts, then applies built-in guardrails to validate the data before passing it to sales. This setup prevents the agent from processing junk data or hallucinating lead scores. The OpenAI guardrail layer checks the output of `list_conversations` to ensure your team only gets verified prospect information.

Map organizational routing in real time

The `list_teams` tool exposes your internal support structure directly to your OpenAI Agents SDK workflow. When a hot lead comes in, the agent queries this tool to find the right department and updates the assignment. If a team structure changes, the agent queries `get_bot` to verify which bot feeds which department. This keeps your routing logic aligned with your actual team layout without hardcoding routing rules in Python.

Setup guide

Set up ChatGen 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 ChatGen tools at runtime.
3

Create your Agent

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

Run the agent

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

Yes, every tool call is traced directly. You can monitor `list_conversations` and `get_lead_details` execution times directly in your OpenAI developer dashboard. This gives you complete visibility into agent latency and tool usage.

You register the server using `MCPServerStreamableHttp` with your connection URL. Pass this instance inside the `mcp_servers` list when initializing your agent. The SDK handles tool discovery automatically at runtime.

Yes, you can build an agent that runs `list_leads` and checks profiles using `get_lead_details`. The agent can then trigger actions based on conversation history fetched via `list_conversations`.

No, it doesn't. The integration uses the standard MCP protocol to expose schemas for tools like `create_bot` and `update_bot` automatically. Your agent understands the arguments without manual configuration.

All conversation logs, lead details, and bot configurations remain sandboxed in the Vinkius V8 isolate. The SDK only receives the specific data returned by tools like `get_lead_details` over an encrypted HTTP transport. No raw database access is ever exposed to the model.

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