How to Use the Zeev MCP in OpenAI Agents SDK

Q: How do I check if a specific process is running using Zeev and OpenAI Agents SDK?

You use the getrequest tool to pull up all the details of an active workflow instance. This lets your agent confirm exactly where in the BPMN flow the request currently sits.

Q: Where can I find a list of all available workflows for Zeev and OpenAI Agents SDK?

The listprocesses tool provides an exhaustive catalog. It shows every defined process definition that your agent can interact with, making sure it knows what's possible.

Q: If a user needs to know their current identity for Zeev and OpenAI Agents SDK?

The getme tool retrieves the current user's information. This is crucial when your agent needs to know who it's working on behalf of before delegating tasks.

Q: What if I need to track a specific task in Zeev and OpenAI Agents SDK?

You can get granular details about any single job using gettask. This pinpoints exactly what needs attention, which is key for complex approval chains.

Manage complex, digitized business flows with the OpenAI Agents SDK.

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Connect Zeev MCP to OpenAI Agents SDK

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

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Start and manage processes.

You initiate a new workflow using `create_request`. This function handles starting process requests in Zeev, giving your agent a clear entry point into complex business logic. Once running, you can check the status or details of any instance by calling `get_request`.

Handle task assignments and completions.

When work needs to move, you use `delegate_task` to hand off a job to another user. After that person finishes the work, your agent calls `finish_task`, officially completing the step in Zeev's workflow. You can also check who should be working by listing pending jobs with `list_tasks`.

Monitor and control process state.

If things go sideways, you don't want to wait around. Your agent can stop a running flow instantly using `cancel_request`. Otherwise, it keeps tabs on what's going on by getting an overview of the definition or listing all current processes with `list_processes`.

Setup guide

Set up Zeev 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 Zeev tools at runtime.
3

Create your Agent

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

Run the agent

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

You use the `get_request` tool to pull up all the details of an active workflow instance. This lets your agent confirm exactly where in the BPMN flow the request currently sits.

The `list_processes` tool provides an exhaustive catalog. It shows every defined process definition that your agent can interact with, making sure it knows what's possible.

The `get_me` tool retrieves the current user's information. This is crucial when your agent needs to know who it's working on behalf of before delegating tasks.

You can get granular details about any single job using `get_task`. This pinpoints exactly what needs attention, which is key for complex approval chains.

The server manages processes, requests, and tasks. It touches process definitions, request instances, and task records. Your agent can trust that these are handled via the secure endpoint token.

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