How to Use the Azure Blob Container MCP in OpenAI Agents SDK

Q: How do I filter files in my Azure Blob Container using OpenAI Agents SDK?

Pass a string to the prefix parameter in listblobs. The server returns only the files matching that path.

Direct file management in OpenAI Agents SDK. Manage your storage without leaving the Python environment.

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Connect Azure Blob Container MCP to OpenAI Agents SDK

Create your Vinkius account to connect Azure Blob Container 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 Azure Blob Container with OpenAI Agents SDK

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Direct file operations in OpenAI Agents SDK

Your agent reads and writes files directly to your storage using `put_blob` and `get_blob`. It handles the binary data transfer through the SDK runtime so you keep your logic inside your Python scripts. Safety guardrails in your agent configuration prevent unauthorized writes. You define the scope and the MCP Server handles the execution against the container.

Manage file lists with OpenAI Agents SDK

Call `list_blobs` to see exactly what is sitting in your container. You can pass a prefix to target specific subdirectories, keeping the agent focused on relevant data. This keeps the context window clean by only pulling in files the agent actually needs. It prevents the model from guessing filenames.

Controlled deletion in OpenAI Agents SDK

Use `delete_blob` to purge outdated logs or temporary files. The tool requires a specific blob path to avoid accidental wipes. Because this is a production system, you should wrap this tool in an agent-level confirmation step. It's the only way to ensure you don't lose critical data.

Setup guide

Set up Azure Blob Container 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 Azure Blob Container tools at runtime.
3

Create your Agent

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

Run the agent

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

You should use a scoped Vinkius endpoint token. This restricts the MCP Server to the specific container you designate for your OpenAI Agents SDK agent.

Yes, it detects the four tools automatically. Your OpenAI Agents SDK code sees the functions immediately after you pass the server to the constructor.

Absolutely, the connection is built for async execution. Your OpenAI Agents SDK loop will handle the network IO without blocking the main thread.

Pass a string to the prefix parameter in `list_blobs`. The server returns only the files matching that path.

All binary file content is encrypted in transit between the server and your agent. Your Azure Blob Container credentials never touch the OpenAI Agents SDK memory space.

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