How to Use the HrFlow.ai MCP in OpenAI Agents SDK

Q: Which HrFlow.ai tools work best for multi-agent setups in OpenAI Agents SDK?

Assign parseprofile to a dedicated intake agent. Pass the parsed output to a secondary agent equipped with scoreprofiles for evaluation.

Build production-grade recruiting agents with OpenAI Agents SDK that parse and score candidates safely.

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Connect HrFlow.ai MCP to OpenAI Agents SDK

Create your Vinkius account to connect HrFlow.ai 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 HrFlow.ai with OpenAI Agents SDK

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Route candidates across OpenAI Agents SDK

You need structured data before an agent can evaluate a candidate. The `parse_profile` tool takes raw resume files and turns them into clean JSON strings. This MCP connection handles the heavy lifting of document extraction so your code focuses entirely on routing logic. Once parsed, your intake agent hands the data off to a specialized scoring agent. The `list_sources` and `list_profiles` tools let your agents verify exactly where the candidate originated before running background tasks. Handoffs work perfectly because the data shape remains consistent.

Evaluate talent with strict guardrails

Unchecked AI hiring creates massive compliance risks. Using `score_profiles`, your agent evaluates an applicant against a specific job description. The OpenAI framework enforces built-in guardrails that validate the agent's logic before it rejects anyone. You can trace exactly why a candidate received a specific rating. If a recruiter questions a decision, the agent uses `ask_profile` to pull exact quotes from the resume that justify the match score. Transparency comes standard.

Map career trajectories via MCP Server

Standard keyword matching misses the actual story of a candidate's career. The `unfold_profile` tool analyzes past roles to predict future capabilities. It reads the progression timeline rather than just scanning for job titles. Combine this with `search_jobs` to automatically match passive candidates to new openings. Your agent searches the HrFlow boards via `list_boards` and surfaces roles the candidate never even considered. You find better talent without manual sourcing.

Setup guide

Set up HrFlow.ai 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 HrFlow.ai tools at runtime.
3

Create your Agent

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

Run the agent

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

Use the MCPServerStreamableHttp class. Pass the endpoint URL into the parameters and provide it to your Agent constructor.

Yes. Wrap the server initialization in an async context manager. Your agent handles the concurrent requests natively.

Assign `parse_profile` to a dedicated intake agent. Pass the parsed output to a secondary agent equipped with `score_profiles` for evaluation.

It does. Set cacheToolsList to true for better performance, and the SDK will map all ten HrFlow tools automatically.

This MCP Server processes highly sensitive PII, including work histories, contact details, and education records. The OpenAI framework's tracing allows you to monitor exactly which resume fields your agents access and log.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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