How to Use the Flow MCP in OpenAI Agents SDK

Q: How do I register the Flow MCP Server in OpenAI Agents SDK?

Install the SDK first using pip. Then, initialize MCPServerStreamableHttp with your Vinkius endpoint URL and pass it to your Agent constructor in the mcpservers list.

Q: Can OpenAI Agents SDK manage multiple Flow workspaces?

Yes, the agent calls listworkspaces to discover all accessible areas. It then uses the workspace IDs to target operations like listprojects for specific teams.

Q: How does OpenAI Agents SDK handle Flow rate limits?

The SDK handles basic network retries, but you should set cacheToolsList=True during initialization. This prevents the agent from making redundant schema requests before calling listtasks.

Build production-grade Python agents with OpenAI Agents SDK that manage your Flow workspaces with strict guardrails.

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Connect Flow MCP to OpenAI Agents SDK

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

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Secure task triaging with OpenAI Agents SDK

The Flow MCP Server exposes `create_task` and `update_task` directly to your OpenAI Agents SDK runtime. Your agent reads incoming team requests, checks the project structure, and writes clean tasks directly to your boards. You write strict Python guardrails to inspect these modifications before they hit the API. By combining `get_project` with OpenAI's native validation, your agent avoids creating duplicate tickets or assigning work to the wrong workspace.

Team routing using OpenAI Agents SDK

This MCP Server lets your OpenAI Agents SDK setup inspect team structures using `list_workspace_members` and `list_workspace_teams`. When a new project kicks off, one specialized agent queries the roster while another assigns tasks. OpenAI's native agent handoffs pass the context from the roster search directly to the task creator. This setup uses `add_task_comment` to log exactly which agent made the assignment, keeping a clear audit trail in your workspace.

Workspace auditing via OpenAI Agents SDK

Your OpenAI Agents SDK pipeline uses `list_workspaces` and `list_projects` to map out your entire team structure. The agent pulls live data to build custom status reports without manual exporting. Because this MCP Server runs in a secure V8 sandbox, your API keys stay hidden from the agent itself. The agent only sees the tools it needs, like `get_task` and `list_task_comments`, to compile accurate progress updates.

Setup guide

Set up Flow 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 Flow tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the SDK first using pip. Then, initialize `MCPServerStreamableHttp` with your Vinkius endpoint URL and pass it to your Agent constructor in the `mcp_servers` list.

Yes, the agent calls `list_workspaces` to discover all accessible areas. It then uses the workspace IDs to target operations like `list_projects` for specific teams.

The SDK handles basic network retries, but you should set `cacheToolsList=True` during initialization. This prevents the agent from making redundant schema requests before calling `list_tasks`.

Yes, you control tool exposure at the agent definition level in Python. You can choose to expose only `add_task_comment` and `get_task` if you want a read-only or comment-only agent.

Your task names, project lists, and comments are protected by Vinkius's zero-trust sandbox. The SDK connects via a single HTTP token, meaning your credentials never touch the raw LLM context or log files.

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