How to Use the Lever MCP in OpenAI Agents SDK

Q: Does the Lever MCP Server expose personal candidate info?

Yes, tools like getcandidateprofile return candidate names and contact details. We treat this data as sensitive and recommend limiting agent permissions.

Connect your OpenAI Agents SDK to Lever to manage your hiring pipeline without leaving your Python environment.

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Connect Lever MCP to OpenAI Agents SDK

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

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Automated candidate stage updates

Stop manually shifting candidates across your funnel. Your agent uses `update_opportunity_stage` to advance applicants the second they pass a technical screen. This keeps your pipeline moving while the OpenAI Agents SDK handles the logic. You get faster turnarounds without touching the dashboard.

Real-time job posting management

Keep your listings accurate by syncing your internal data directly to Lever. Your agent calls `create_job_posting` and `list_job_postings` to reflect current headcount needs. Everything stays in your code. You define the hiring requirements, and the agent ensures the ATS matches your current headcount plan.

Direct candidate data access

Your agent can pull specific applicant details whenever it needs context. It uses `get_candidate_profile` and `get_opportunity_details` to verify applicant info before making a move. This MCP Server provides the raw data your agent needs to make informed decisions. It pulls the profile directly, so you never guess about a candidate's status.

Setup guide

Set up Lever 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 Lever tools at runtime.
3

Create your Agent

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

Run the agent

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

You initialize the connection using the MCPServerStreamableHttp class. Pass your server URL to the constructor and add it to the mcp_servers list in your Agent setup.

The server exposes tools that your agent invokes on demand. You trigger the action from your agent logic, and the Lever MCP Server executes it against your ATS.

Your agent only accesses the endpoints you explicitly allow. The connection uses your private token, and no data is shared outside your environment.

The MCP protocol transmits the response directly to your agent. Your code handles the data structure provided by the API endpoints.

Yes, tools like `get_candidate_profile` return candidate names and contact details. We treat this data as sensitive and recommend limiting agent permissions.

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