How to Use the Correios API (REST) MCP in LangChain

Build complex shipping logic into your LangChain chains with the Correios API (REST) MCP Server.

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Connect Correios API (REST) MCP to LangChain

Create your Vinkius account to connect Correios API (REST) to LangChain 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 API (REST) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain shipping quotes in LangChain

Pipe live postal data directly into your agent reasoning loops. The `calculate_price_deadline` tool feeds exact shipping costs into your next chain link. Your LangChain agent decides when to pull these rates based on previous user input. You get full visibility into the request lifecycle via LangSmith tracing.

Automate package status updates

Feed tracking events from the `track_object` tool directly into your data processing pipelines. Your agent parses the response to update internal state without manual intervention. You avoid clunky manual lookups by letting the agent chain the tracking ID to a notification step. This works best when you need to trigger logic based on specific transit milestones.

Build multi-server shipping agents

Aggregate this MCP Server alongside your other database tools within a single LangChain agent. You define the sequence and the agent handles the rest. This architecture keeps your shipping logic modular. You swap out services without breaking the core reasoning chain.

Setup guide

Set up Correios API (REST) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Correios API (REST) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "correios-api-rest-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Correios API (REST) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Correios API (REST) MCP in LangChain

You install the MCP adapters and point the client to your Vinkius endpoint. From there, you call get_tools and inject them into your agent definition.

Absolutely, the tools are designed for chains. One step queries the price, and the next step uses that number to finalize a customer order.

Yes, the agent observes the output of each tool call. It evaluates the postal data before deciding if more actions are required.

The server remains stateless by default. You control persistence by using the client session object to keep context across turns.

We isolate your tracking requests within a Vinkius sandbox. Only the object codes you pass to the tool are transmitted to the postal service.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.