How to Use the Cloud Assess MCP in OpenAI Agents SDK

Get real-time student and enrolment data directly in your OpenAI Agents SDK pipeline with this secure MCP Server.

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Connect Cloud Assess MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cloud Assess 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 Cloud Assess with OpenAI Agents SDK

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Track student progress via OpenAI Agents SDK

Pass `list_unit_enrolment_progress` and `get_student_details` straight to your Python-based OpenAI agents. Your system queries live vocational training metrics, allowing your agents to make decisions based on actual unit completion rates without manual lookups. The MCP Server registers these tools automatically when you boot your agent. This setup means your agent can instantly pull up student records, map out progress, and flag overdue modules on the fly.

Run secure assessor audits with built-in guardrails

Use `list_training_assessors` and `list_assessment_records` to monitor who is marking which papers. Your agent checks assessor workloads and matches them against active student submissions to spot bottlenecks in your training pipeline. Because you are using the OpenAI Agents SDK, you can set hard guardrails on these tools. This prevents your agent from modifying records or leaking sensitive staff rosters during automated assessments.

Multi-agent coordination for training locations

Coordinate physical training logistics by feeding `list_training_locations` and `list_training_enrolments` to specialized agents. One agent handles room booking while another assigns students, passing context back and forth using native SDK handoffs. Every single MCP tool call shows up in your OpenAI developer dashboard. You see exactly how the agents query location capacities and enrolment counts, making debugging simple.

Setup guide

Set up Cloud Assess 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 Cloud Assess tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the SDK via pip and pass the MCP Server URL directly into your streamable HTTP server parameters. The SDK auto-discovers all eight tools, including `list_student_members`, making them immediately available to your agents.

Yes, you control tool access by configuring the agent's tool list during initialization. You can expose only specific tools like `list_assessment_forms` to keep your agent focused on auditing tasks.

The SDK catches the error and reports the failure back to the calling agent. You can track the exact error payload and tool execution steps directly inside your OpenAI tracing dashboard.

No, the MCP Server handles all schema definitions out of the box. Your agent reads the tool definitions and parameters automatically, so you write zero boilerplate code.

All student enrolment and assessment data remains inside the Vinkius zero-trust sandbox. We run each session in an ephemeral V8 isolate, meaning your training records are never stored on our servers or used for model training.

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