How to Use the cloudlayer.io MCP in OpenAI Agents SDK

Q: How do I add the cloudlayer.io MCP Server to my OpenAI Agents SDK project?

Install the package using pip install openai-agents. Initialize MCPServerStreamableHttp with your Vinkius endpoint URL and pass it to the Agent constructor in the mcpservers list. Set cacheToolsList=True to speed up tool discovery during agent startup.

Q: Can an agent verify its previous document runs using cloudlayer.io and OpenAI Agents SDK?

Yes. Your agent calls listgenerationhistory to inspect past PDF and screenshot jobs. This lets the agent verify if a specific document was already generated before initiating a new billing charge.

Generate pixel-perfect PDFs and screenshots directly from your OpenAI Agents SDK production pipelines.

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Connect cloudlayer.io MCP to OpenAI Agents SDK

Create your Vinkius account to connect cloudlayer.io 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 cloudlayer.io with OpenAI Agents SDK

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Safe PDF rendering inside OpenAI Agents SDK

Your production agents need guardrails when generating customer-facing documents. By mounting this MCP Server, your agent calls `convert_html_to_pdf` only after validating the raw input data against your safety policies. The OpenAI dashboard traces the entire execution so you see exactly what raw HTML triggered the PDF generation. When an agent needs to confirm a layout, it hands the task off to a specialized visual validation agent. This secondary agent runs `capture_url_screenshot` to inspect the live page and verify the rendering before completing the user's request.

Automated template management

Instead of hardcoding document layouts in Python, let your agents manage dynamic Nunjucks layouts on the fly. Your agent queries your active layouts using `list_pdf_templates` to select the correct design for a client invoice or receipt. Once the agent identifies the layout, it pulls the exact structure with `get_template_configuration`. This lets the agent populate the required variables with zero manual mapping, keeping your production code clean and database-driven.

Usage tracking and async notification loops

High-volume document generation requires strict quota monitoring to prevent API failures in production. Your agent monitors your team's real-time API limits using `get_cloudlayer_usage_stats` and pauses heavy generation tasks before hitting hard caps. For long-running document compilation, the agent checks your notification setup with `list_cloudlayer_webhooks`. This ensures your callback endpoints are active, letting your agent handle PDF delivery asynchronously without blocking your primary thread.

Setup guide

Set up cloudlayer.io 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 cloudlayer.io tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the package using `pip install openai-agents`. Initialize `MCPServerStreamableHttp` with your Vinkius endpoint URL and pass it to the Agent constructor in the `mcp_servers` list. Set `cacheToolsList=True` to speed up tool discovery during agent startup.

Yes. Your agent calls `list_generation_history` to inspect past PDF and screenshot jobs. This lets the agent verify if a specific document was already generated before initiating a new billing charge.

This usually happens if the target URL is behind a login wall or has slow response times. Ensure the URL is publicly accessible, or have your agent pass raw HTML to `convert_html_to_pdf` instead to avoid network timeouts.

You can define one agent dedicated to data gathering and another to document compilation. The first agent gathers raw data and hands off to the rendering agent, which executes the `convert_html_to_pdf` tool safely.

Your raw HTML payloads and rendered PDF files are processed over encrypted TLS connections. Vinkius runs this MCP Server in an isolated sandbox, meaning your document data never persists in the runtime environment after the generation job completes.

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