How to Use the Correios (Brazil) MCP in OpenAI Agents SDK

Plug Correios (Brazil) logistics directly into your OpenAI Agents SDK pipelines for instant tracking and rate estimation.

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Connect Correios (Brazil) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Correios (Brazil) 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 Correios (Brazil) with OpenAI Agents SDK

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Perplexity

Automate shipping logistics in OpenAI Agents SDK

Your agent queries `calculate_price_deadline` to get exact costs before finalizing any shipment. It handles the math so your code doesn't have to. This MCP Server feeds the result back into your agent's state. You avoid manual lookups and keep your internal logistics logic clean.

Track shipments via OpenAI Agents SDK

Feed a tracking code into `track_object` to pull the current status of any package. Your agent parses the response and updates the user immediately. This replaces polling manual web forms. You get the raw status data directly in your Python environment.

Validate Brazilian addresses with OpenAI Agents SDK

Use `lookup_cep` to confirm any Brazilian ZIP code before sending data to your back-end. It stops bad addresses from entering your database. Your agent checks the location details returned by the tool. It ensures your client-facing data matches the official registry.

Setup guide

Set up Correios (Brazil) 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 Correios (Brazil) tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Correios (Brazil) 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="Correios (Brazil) Agent",
            instructions="You have access to Correios (Brazil) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Correios. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Correios (Brazil) MCP in OpenAI Agents SDK

Install the library and define an MCPServerStreamableHttp instance. Pass this server object into your Agent constructor to let it discover the tools.

Yes, the SDK consumes the tool outputs directly as Python dictionaries. You can map these values into your own agent-side logic.

The guardrails validate the inputs your agent sends to the tools. You can restrict which functions the agent calls to prevent unauthorized shipping calculations.

Vinkius manages the endpoint authentication via a single token. Your agent never touches raw API keys for the carrier services.

Your package tracking codes move through an encrypted tunnel. Vinkius keeps this PII isolated from your model training logs.

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