How to Use the MagicDrip MCP in OpenAI Agents SDK

Q: How does this setup protect my prospect data?

When your agent calls getleadoutreachdetails, the prospect names and LinkedIn profile URLs route through a V8 Isolate Sandbox. The environment is completely ephemeral and dies the second the OpenAI agent finishes the task.

Build production-grade LinkedIn outreach agents with the OpenAI Agents SDK. Enforce strict guardrails on every connection request.

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Connect MagicDrip MCP to OpenAI Agents SDK

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

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Perplexity

Enforce limits in OpenAI Agents SDK

You don't want an autonomous agent spamming LinkedIn until your account gets banned. That's why building with this framework matters. You can set strict guardrails that force your agent to verify limits using `get_available_slots_quota` before it does anything else. If the quota looks good, the agent proceeds to pull targets with `list_outreach_leads`. The OpenAI dashboard traces every single step. You see exactly when the agent decided it was safe to act and when it backed off.

Manage multi-agent outreach

Handoffs make this setup actually viable for production. You can have a research agent pull data with `get_campaign_performance` and analyze the metrics. When it finds a winning sequence, it hands the context over to an execution agent. That second agent handles the actual sending. It triggers `send_linkedin_invite` or `send_direct_linkedin_message` based on the research agent's instructions. Splitting the work keeps the context windows clean and the execution predictable.

Connect the MagicDrip MCP Server

Wiring this up takes exactly one context manager. You pass the MCP Server URL into the streamable HTTP class and hand it to the Agent constructor. The SDK auto-discovers the entire toolset instantly. Just remember to set the cache flag to true so you aren't fetching definitions on every run. Once connected, your agent can immediately hit `check_api_health` or grab webhook endpoints via `list_configured_webhooks` without writing manual API wrappers.

Setup guide

Set up MagicDrip 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 MagicDrip tools at runtime.
3

Create your Agent

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

Run the agent

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

You don't pass credentials to the SDK directly. Vinkius handles the authentication at the server level. You just give the agent your single endpoint token.

Yes, if you configure the agent's behavior to handle errors from `send_linkedin_invite`. The built-in guardrails will catch the failure and trigger a retry based on your logic.

Open your OpenAI dashboard. The MCP integration logs every call to `add_linkedin_lead` or `list_outreach_campaigns` automatically so you can trace the execution.

Yes. Using the streamable HTTP transport enables the agent to handle large payloads from `list_outreach_leads` asynchronously. You won't block your main thread waiting for pagination.

When your agent calls `get_lead_outreach_details`, the prospect names and LinkedIn profile URLs route through a V8 Isolate Sandbox. The environment is completely ephemeral and dies the second the OpenAI agent finishes the task.

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