How to Use the FirstQuadrant MCP in OpenAI Agents SDK

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Connect FirstQuadrant MCP to OpenAI Agents SDK

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

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Claude

Perplexity

Automate lead creation and enrichment

Stop wasting time on manual data entry. Use `create_contact` to push leads directly from your agent into your pipeline. Trigger `enrich_contact_ai` immediately after creation to pull professional data. Your agent populates CRM fields without you lifting a finger.

Manage campaign enrollment

Keep your pipeline moving by using `add_to_campaign` for every qualified lead. Your agent assigns prospects to sequences based on real-time triggers. Audit your progress using `list_campaigns` to verify lead placement. This MCP Server ensures your agent maintains perfect CRM hygiene.

Track and analyze sales activity

Query `list_activities` to get a clear view of your outbound performance. Your agent uses this data to refine its next move. Use `list_contacts` with custom filters to find specific records. Your agent identifies stalled leads before they fall through the cracks.

Setup guide

Set up FirstQuadrant 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 FirstQuadrant tools at runtime.
3

Create your Agent

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

Run the agent

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

You connect the server via the agent constructor using the provided HTTP endpoint. The SDK automatically discovers all eight tools, allowing your agent to call them immediately.

Yes. FirstQuadrant processes contacts in real-time, and you can use `list_contacts` to batch your operations effectively. Just monitor your API limits to keep things stable.

The SDK handles the logic, while FirstQuadrant provides the raw data. You should implement your own validation logic before calling `create_contact` to ensure high data quality.

We use encrypted transport for all data moving between your agent and our infrastructure. Your endpoint token keeps the link private.

We store professional contact details and campaign history. Your agent only accesses this data when you explicitly invoke a tool.

Use it with your favorite AI tools

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OpenAI Agents SDK sdk-python

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