How to Use the LeadConnector MCP in OpenAI Agents SDK

Q: How do I configure the LeadConnector MCP Server inside the OpenAI Agents SDK?

Install openai-agents and initialize MCPServerStreamableHttp with your Vinkius endpoint URL. Pass this server instance into the Agent constructor using the mcpservers list. The SDK automatically discovers the tools.

Q: How does the OpenAI Agents SDK handle LeadConnector rate limits?

Use cacheToolsList=True in your setup to prevent constant tool discovery requests. For heavy data pulling, write a basic loop in Python to pace calls to listcontacts and avoid hitting API thresholds with this MCP setup.

Give your OpenAI Agents SDK production pipelines direct access to LeadConnector contacts, deals, and calendar schedules.

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Connect LeadConnector MCP to OpenAI Agents SDK

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

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Connect the LeadConnector MCP Server in production

This integration uses `list_contacts` to hook directly into your OpenAI Agents SDK runtime to pull fresh contacts. You avoid writing custom API wrappers or handling token refreshes manually. The agent identifies callers before drafting responses, saving precious milliseconds during live customer interactions.

Automate calendar checks with native guardrails

By combining OpenAI Agents SDK guardrails with `list_appointments`, your agent checks actual availability before proposing times. The system blocks invalid scheduling attempts before they hit the API. You define the validation rules in Python, and the agent schedules meetings without human intervention.

Trace opportunity updates in your agent dashboard

Track how your agents interact with sales pipelines by monitoring calls to `list_opportunities`. Every API call, payload, and response latency shows up in your OpenAI tracing dashboard. Turn on `cacheToolsList=True` to speed up tool discovery and keep your pipeline running fast.

Setup guide

Set up LeadConnector 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 LeadConnector tools at runtime.
3

Create your Agent

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

Run the agent

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

Install `openai-agents` and initialize `MCPServerStreamableHttp` with your Vinkius endpoint URL. Pass this server instance into the `Agent` constructor using the `mcp_servers` list. The SDK automatically discovers the tools.

Yes, you control this at the SDK level by filtering the tools passed to your agent. If you only want the agent to read deals, expose `list_opportunities` and omit the calendar or contact tools from the initialization configuration.

Use `cacheToolsList=True` in your setup to prevent constant tool discovery requests. For heavy data pulling, write a basic loop in Python to pace calls to `list_contacts` and avoid hitting API thresholds with this MCP setup.

Use the async context manager provided by the SDK. This ensures connections to the streamable HTTP endpoint close properly when your pipeline finishes executing.

Your contacts, opportunities, and calendar details pass through Vinkius's zero-trust V8 sandbox directly to your Python runtime. No data is stored or cached on our servers. The connection is ephemeral and encrypted.

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