How to Use the Lusha MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent use Lusha tools for bulk tasks?

Yes, you call bulkenrich to process your lead lists. The agent handles the requests asynchronously, keeping your main thread responsive.

Q: What happens if my Lusha credits run out during an agent run?

Your agent catches the API error when calling getcredits. You should implement a fallback in your code to pause the task and notify your team.

Plug Lusha into your OpenAI Agents SDK production pipeline to automate lead enrichment with verified B2B contact data.

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Connect Lusha MCP to OpenAI Agents SDK

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

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Automated prospect enrichment

Stop manual data entry and let your agent handle the heavy lifting. You can trigger `bulk_enrich` to process lists directly within your agent workflow, keeping your CRM records clean without switching tabs. This keeps your pipeline moving while the agent handles the grunt work. It pulls verified data straight from the source to keep your outbound stats accurate.

Targeted profile lookups

Feed LinkedIn URLs into `find_by_linkedin` to get immediate contact insights. Your OpenAI Agents SDK agent maps profiles to verified emails and phone numbers in real-time. You define the logic, and the agent executes the lookups. It cuts out the middleman and gives your sales process the speed it actually needs.

Real-time credit management

Keep your agent within budget by checking `get_credits` before executing high-volume tasks. It prevents runaway API costs during your agent's operation. Your code monitors the balance automatically. You avoid service interruptions by setting threshold alerts directly in your Python logic.

Setup guide

Set up Lusha 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 Lusha tools at runtime.
3

Create your Agent

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

Run the agent

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

Use the MCPServerStreamableHttp class to connect your agent. Pass the server instance into your agent constructor and let the SDK handle the tool discovery.

Yes, you call `bulk_enrich` to process your lead lists. The agent handles the requests asynchronously, keeping your main thread responsive.

It plugs in as a standard toolset. Your agent decides when to use `find_person` or `search_contacts` based on the context you provide in your prompts.

The server uses ephemeral sessions and secure token handling. Your agent only sees the data returned from the specific tool calls you authorize.

Your agent catches the API error when calling `get_credits`. You should implement a fallback in your code to pause the task and notify your team.

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