How to Use the HiFlow MCP in OpenAI Agents SDK

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

Install openai-agents via pip. Create an MCPServerStreamableHttp instance with your endpoint URL. Pass it into your agent constructor under the mcpservers array.

Q: Can OpenAI Agents SDK create new customers in HiFlow?

Yes. The createcustomer tool allows your agent to write new client records. You should configure SDK guardrails to require human approval before this tool fires.

Q: Does this support agent handoffs?

Yes. One agent can run getjob to gather project context. It can then pass that specific job ID to a secondary agent tasked with generating reports.

Give your OpenAI Agents SDK production-grade access to the HiFlow MCP Server.

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Connect HiFlow MCP to OpenAI Agents SDK

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

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Run HiFlow operations via OpenAI Agents SDK

Your agent needs customer context before it acts. Passing the HiFlow MCP Server to your agent constructor gives it immediate access to `list_customers` and `get_customer`. You get real-time client details without writing custom API wrappers. Handoffs work perfectly here. A triage agent pulls client history, then routes the task to a specialized billing agent that runs `list_jobs` to verify active projects. Every tool call shows up in your OpenAI tracing dashboard.

Validate invoices before execution

Production systems require strict guardrails around financial data. When your agent attempts to read `get_invoice` or `get_estimate`, the SDK built-in safety checks evaluate the action against your constraints. You decide what the agent is allowed to touch. Pulling bulk data is just as secure. The agent can aggregate outstanding balances using `list_invoices` and `list_estimates`. If a step violates your predefined rules, the SDK blocks the execution before the request ever hits the server.

Track team activity autonomously

Building a reporting agent means giving it raw access to work logs. The `list_timesheets` tool feeds raw hour entries directly into the model context. Developers hate parsing pagination logic manually. Add `get_activity` and `get_me` to build a complete picture of who did what. The SDK handles tool discovery automatically. Just set `cacheToolsList=True` to keep latency low when your agent hits these endpoints repeatedly.

Setup guide

Set up HiFlow 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 HiFlow tools at runtime.
3

Create your Agent

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

Run the agent

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

Install `openai-agents` via pip. Create an `MCPServerStreamableHttp` instance with your endpoint URL. Pass it into your agent constructor under the `mcp_servers` array.

Yes. The `create_customer` tool allows your agent to write new client records. You should configure SDK guardrails to require human approval before this tool fires.

Tool discovery happens automatically, but network issues can block it. Check your server URL and token. Ensure you initialized the MCP parameters correctly in your Python script.

Yes. One agent can run `get_job` to gather project context. It can then pass that specific job ID to a secondary agent tasked with generating reports.

The V8 Isolate Sandbox ensures your timesheets and invoices remain isolated during execution. Tokens are ephemeral, meaning credentials die the second the session ends. No persistent data storage occurs on the middleware layer.

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