How to Use the IBGE Censo & SIDRA — Dados Censitários MCP in LangChain

Build automated pipelines for Brazilian economic analysis by integrating IBGE Censo & SIDRA — Dados Censitários into your LangChain chains.

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Connect IBGE Censo & SIDRA — Dados Censitários MCP to LangChain

Create your Vinkius account to connect IBGE Censo & SIDRA — Dados Censitários 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 IBGE Censo & SIDRA — Dados Censitários with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain together official Brazilian statistical data

Feed the output of `list_agregados` directly into subsequent logic to identify relevant tables for your research. Your agent determines the sequence of calls without manual intervention. This setup ensures your LangChain workflow moves from discovery to precise data extraction. You define the logic, and the server provides the raw numbers for every step of your pipeline.

Automate temporal analysis for economic modeling

Use `get_agregado_periodos` to establish valid time ranges before you pull data. This prevents errors when your agent queries specific historical intervals. By linking these calls, you construct a resilient chain that respects the temporal structure of the SIDRA API. Your model gets the context it needs to generate accurate trend projections.

Extract granular metrics with precision

Trigger `get_agregado_data` to pull specific variables like inflation or GDP directly into your execution flow. This tool allows your agent to fetch targeted figures based on your defined filters. You avoid parsing bloated datasets by requesting only what you need. It turns raw government statistics into immediate input for your agent's decision-making process.

Setup guide

Set up IBGE Censo & SIDRA — Dados Censitários 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 IBGE Censo & SIDRA — Dados Censitários 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({
    "ibge-censo-sidra-dados-censitarios-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 IBGE Censo & SIDRA — Dados Censitários transactions"
    })
    print(result["messages"][-1].content)

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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 IBGE. 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 IBGE Censo & SIDRA — Dados Censitários MCP in LangChain

Install the necessary adapters and initialize the client with your endpoint. Once configured, you expose the tools to your agent for seamless interaction with the SIDRA API.

Yes. Your agents can call these tools sequentially, using the output from a table list to inform a subsequent data request.

Absolutely. Every call to the SIDRA interface is fully observable through LangSmith, allowing you to debug latency and input parameters in real-time.

The server manages requests efficiently, but you should implement retry logic in your chain if you expect high-volume polling of the SIDRA database.

No. The server acts as a transient bridge to the public SIDRA API. It does not persist your data requests or store any demographic information locally.

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