How to Use the Greenhouse MCP in OpenAI Agents SDK

Q: How do I connect Greenhouse to the OpenAI Agents SDK?

Install openai-agents and initialize MCPServerStreamableHttp with your Vinkius endpoint. Pass the server instance inside the mcpservers list when instantiating your Agent. Your tools auto-discover instantly.

Q: Can OpenAI Agents SDK handle Greenhouse rate limits?

Yes, by setting cacheToolsList=True in your SDK configuration. This prevents the agent from making redundant schema requests, saving your Greenhouse API quota for actual data operations like listcandidates.

Q: How do I restrict OpenAI Agents SDK write access in Greenhouse?

Configure your Vinkius gateway to block write tools like createcandidate. Your OpenAI Agents SDK will only discover read-only tools like listjobs and listdepartments, keeping your pipeline secure.

Q: How does agent handoff work with this MCP integration?

One agent can fetch candidate details using getcandidate, then hand the context to a scheduling agent. The second agent uses listjobstages to determine the next steps without losing the original candidate context.

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Connect Greenhouse MCP to OpenAI Agents SDK

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

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Screen and ingest applicants with OpenAI Agents SDK

The `create_candidate` tool lets your agent inject new talent profiles directly into your pipeline. By combining this with `list_jobs`, your OpenAI Agents SDK setup maps incoming resumes to open roles without manual data entry. You get complete execution safety because the SDK validates every payload against your Greenhouse schema before hitting the API. This prevents corrupted records from entering your database while maintaining a high processing rate.

Run multi-agent pipeline audits using this MCP Server

The `list_applications` tool feeds active application states directly to your specialized auditing agents. Your OpenAI Agents SDK coordinates handoffs between a sourcing agent and a compliance agent to verify candidate progress. The compliance agent calls `list_job_stages` to compare actual candidate stages against company hiring policies. If a candidate stalls, the SDK logs the exact bottleneck in your OpenAI dashboard trace.

Map internal stakeholders to active job requisitions

The `list_users` tool exposes your internal recruiting team directory to the OpenAI Agents SDK. Your agent calls `get_job` to identify the hiring manager, then cross-references their details using `get_user`. This setup keeps recruiting loops tight by automatically routing status updates to the correct coordinator. You eliminate manual Slack pings since the agent knows exactly who owns each department.

Setup guide

Set up Greenhouse 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 Greenhouse tools at runtime.
3

Create your Agent

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

Run the agent

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

Install `openai-agents` and initialize `MCPServerStreamableHttp` with your Vinkius endpoint. Pass the server instance inside the `mcp_servers` list when instantiating your Agent. Your tools auto-discover instantly.

Yes, by setting `cacheToolsList=True` in your SDK configuration. This prevents the agent from making redundant schema requests, saving your Greenhouse API quota for actual data operations like `list_candidates`.

Configure your Vinkius gateway to block write tools like `create_candidate`. Your OpenAI Agents SDK will only discover read-only tools like `list_jobs` and `list_departments`, keeping your pipeline secure.

One agent can fetch candidate details using `get_candidate`, then hand the context to a scheduling agent. The second agent uses `list_job_stages` to determine the next steps without losing the original candidate context.

Absolutely. Your Greenhouse candidate profiles, application histories, and job details remain inside a zero-trust, ephemeral V8 isolate. Vinkius handles the authentication token securely, meaning your OpenAI Agents SDK never exposes raw credentials to the LLM.

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