How to Use the Workload MCP in OpenAI Agents SDK

Q: Can I monitor historical Workload data using OpenAI Agents SDK?

Absolutely. You can use listexecutions and listlogs to pull every past job record. This lets you build reporting dashboards that show exactly what happened, when it happened, and if any steps failed.

Q: How does the Workload server manage multiple processes within OpenAI Agents SDK?

The system treats every process independently. You can use listworkflows to get a master list of all active automations, and then target specific ones using their unique IDs for management.

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Connect Workload MCP to OpenAI Agents SDK

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

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Manage the Full Workflow Lifecycle

When your agent needs to set up a new automated process, it uses `create_workflow` and then tracks its status via `get_workflow`. This means you don't have to manually check if the connection is good; the agent handles the whole setup. You can also shut things down safely using `disable_workflow`, or restart processes that failed by calling `retry_execution`. It’s built for production, so tracking every step matters.

Audit and Trace All Connections

Need to know what's connected? The agent calls `list_connections` to show all available services. If you need historical data, it pulls everything through `list_executions`, letting you trace exactly which workflows ran, when they ran, and who triggered them. This traceability is key for your guardrails. By listing past actions with `list_workflows` or checking the logs using `list_logs`, you keep a full audit trail in the OpenAI dashboard.

Programmatic Status Checks

Before running anything, your agent verifies connectivity by calling `check_workload_status`. This prevents wasted cycles and failed jobs. It's a quick check that ensures the MCP Server is talking to everything it needs to. For advanced debugging, the agent can get granular details on any single job using `get_execution` or retrieve all necessary setup information with `get_connection`.

Setup guide

Set up Workload 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 Workload tools at runtime.
3

Create your Agent

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

Run the agent

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

The agents auto-discover every tool on this MCP Server, so you don't need to write setup code. When an action requires a workflow—like listing jobs or creating a process—your agent calls the appropriate function directly, validating the request before execution.

Absolutely. You can use `list_executions` and `list_logs` to pull every past job record. This lets you build reporting dashboards that show exactly what happened, when it happened, and if any steps failed.

If an agent hits a snag, you don't have to guess. You can use `get_execution` to pull detailed failure reports and then attempt recovery by calling `retry_execution`. It gives you full control over debugging.

The system treats every process independently. You can use `list_workflows` to get a master list of all active automations, and then target specific ones using their unique IDs for management.

This server primarily handles metadata about workflow definitions, execution status, connection details, and logs. It manages the *structure* of your processes, not sensitive user PII.

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