How to Use the Salesquared MCP in OpenAI Agents SDK

Q: Will tool discovery slow down my OpenAI Agents SDK agent?

Not if you set cacheToolsList=True during initialization. This caches the tool definitions locally, which keeps your agent responsive and avoids redundant network calls.

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Connect Salesquared MCP to OpenAI Agents SDK

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

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Lead generation for OpenAI Agents SDK

Your agent creates records in your pipeline without manual intervention. It uses `create_lead` to push new prospects into your database the second they hit your landing page. Everything happens behind the scenes. You don't need to write custom API wrappers because your agent handles the `create_lead` payload directly through the standard protocol.

Automated messaging via Salesquared

Trigger SMS alerts the moment a lead is qualified. Your agent invokes `send_sms` to keep the conversation moving while you focus on closing the deal. This keeps your communication consistent. You rely on the `send_sms` tool to handle the dispatch logic while the agent monitors for delivery status and error responses.

Voice survey and OTP control

Run secure authentication or customer research directly from your agent's decision logic. When a user requests a code, the agent calls `send_voice_otp` to verify identity immediately. You can also launch market research by calling `create_voice_survey`. The agent manages the survey flow so you get clean data without leaving your development environment.

Setup guide

Set up Salesquared 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 Salesquared tools at runtime.
3

Create your Agent

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

Run the agent

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

You connect by passing the server configuration to your agent constructor. Use the `MCPServerStreamableHttp` class and include the endpoint in your `mcp_servers` list. Your agent will auto-discover all available tools immediately.

Yes, and the SDK handles these errors via standard Python exceptions. You should implement try-except blocks around your tool calls to manage cases where `send_sms` or `create_lead` returns a failure status.

It requires a stable connection to communicate with the Vinkius-hosted endpoint. Since it acts as a bridge for your agent, ensure your environment allows outbound HTTP traffic to the server address.

The server uses ephemeral sessions and secure token-based authentication. Your data, specifically lead contact details and SMS logs, never sits on the client side longer than the active request cycle.

Not if you set `cacheToolsList=True` during initialization. This caches the tool definitions locally, which keeps your agent responsive and avoids redundant network calls.

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