How to Use the EmailListVerify MCP in OpenAI Agents SDK

Q: How do I configure EmailListVerify with OpenAI Agents SDK?

Install openai-agents via pip. Create an MCPServerStreamableHttp instance with your endpoint URL and pass it to the mcpservers array in your Agent constructor. Set cacheToolsList=True to speed up tool discovery.

Q: Can OpenAI Agents SDK check my EmailListVerify credit balance?

Yes. The server exposes a getremainingcredits tool. Your agent reads this integer and halts operations if you lack the balance to process a new list.

Q: What happens if a bulk EmailListVerify job fails in OpenAI Agents SDK?

The agent calls getverificationjobstatus to pull the exact result summary for that specific job ID. If the API returns an error state, your configured guardrails catch the failure and stop the workflow.

Connect the EmailListVerify MCP server to your OpenAI Agents SDK system. Build production agents that validate addresses under strict guardrails.

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Connect EmailListVerify MCP to OpenAI Agents SDK

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

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Single email checks with OpenAI Agents SDK

The `verify_single_email` tool connects your OpenAI agent directly to the EmailListVerify API. You pass an address, and the agent returns a definitive deliverability status. This prevents your system from sending outbound messages to dead drops. Production agents need oversight. Since OpenAI Agents SDK supports full tracing, every single verification check appears in your dashboard. You see exactly when the agent decided to validate a contact and what the API returned.

Bulk job monitoring via MCP Server

The `list_verification_jobs` tool gives your agent visibility into every bulk file processing on your account. When you upload a massive list, the agent tracks its progress using `list_in_progress_verification_jobs`. It knows exactly when the queue clears. You don't want agents guessing about file completion. Using `list_successfully_processed_files`, the agent can trigger the next step in your pipeline the second a clean list is ready. You set the guardrails, and the agent handles the waiting game.

Account health and credit tracking

The `get_remaining_credits` tool checks your EmailListVerify balance before the agent attempts large operations. If the balance drops too low, the agent hands off the task to a human operator or triggers an alert. You also get the `quick_verification_health_audit` tool to pull high-level success rates. The agent reads this metadata and adjusts its routing logic based on recent deliverability trends.

Setup guide

Set up EmailListVerify 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 EmailListVerify tools at runtime.
3

Create your Agent

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

Run the agent

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

Install `openai-agents` via pip. Create an `MCPServerStreamableHttp` instance with your endpoint URL and pass it to the `mcp_servers` array in your Agent constructor. Set `cacheToolsList=True` to speed up tool discovery.

Yes. The server exposes a `get_remaining_credits` tool. Your agent reads this integer and halts operations if you lack the balance to process a new list.

The agent calls `get_verification_job_status` to pull the exact result summary for that specific job ID. If the API returns an error state, your configured guardrails catch the failure and stop the workflow.

No. The OpenAI Agents SDK auto-discovers all nine tools from the MCP Server. You just provide the connection parameters, and the agent maps out the endpoints automatically.

The server passes raw email strings and job IDs directly to the external API. Vinkius isolates this execution in an ephemeral V8 sandbox, meaning your contact lists never touch our persistent storage. The environment dies the moment the verification request completes.

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