How to Use the IBGE Agregados (SIDRA) MCP in LlamaIndex

Q: How do I install the IBGE Agregados (SIDRA) tool spec in LlamaIndex?

Run pip install llama-index-tools-mcp to get started. Use BasicMCPClient to connect to your Vinkius endpoint, then pass the tool list to your FunctionAgent.

Q: Can I use LlamaIndex to search the IBGE Agregados (SIDRA) tables semantically?

Yes. You can ingest the output of listagregados directly into a vector store. This lets your agent match natural language user queries to the correct IBGE table ID.

Q: How does this MCP server improve LlamaIndex RAG accuracy?

It provides live access to official metadata using getmetadados. This grounds your agent's responses in real Brazilian economic facts instead of training data.

Index Brazilian census data from IBGE Agregados (SIDRA) into your LlamaIndex vector stores with this MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect IBGE Agregados (SIDRA) MCP to LlamaIndex

Create your Vinkius account to connect IBGE Agregados (SIDRA) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup IBGE Agregados (SIDRA) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Official Datasets using the MCP Server

The `list_agregados` tool fetches lists of official Brazilian surveys and tables to populate your index. LlamaIndex uses this raw text output to build a searchable knowledge base of available statistical topics. Your agent can query this vector index to find the right table ID. This eliminates manual searching through the IBGE SIDRA portal.

Extract Variables and Metadata for RAG

The `get_variaveis` tool extracts specific data fields from an aggregate, and `get_metadados` retrieves its structural metadata. These tools supply the context needed to ground your LLM's answers in actual census definitions. LlamaIndex ingest pipelines convert these schema details into document nodes. This keeps your agent from hallucinating metrics or using incorrect units of measurement.

Filter by Geography and Time Periods

The `get_localidades` tool retrieves valid geographic levels, while `get_periodos` determines what timeframes are available for a given table. These tools provide the filtering parameters for your queries. By loading these parameters into LlamaIndex, your RAG system can verify if a specific regional or temporal data point exists. It ensures your queries don't fail due to out-of-bounds requests.

Setup guide

Set up IBGE Agregados (SIDRA) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all IBGE Agregados (SIDRA) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to IBGE Agregados (SIDRA) tools.",
)
response = await agent.run("List recent IBGE Agregados (SIDRA) data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by IBGE Agregados (SIDRA). 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect IBGE Agregados (SIDRA) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about IBGE Agregados (SIDRA) MCP in LlamaIndex

Run `pip install llama-index-tools-mcp` to get started. Use `BasicMCPClient` to connect to your Vinkius endpoint, then pass the tool list to your `FunctionAgent`.

Yes. You can ingest the output of `list_agregados` directly into a vector store. This lets your agent match natural language user queries to the correct IBGE table ID.

It provides live access to official metadata using `get_metadados`. This grounds your agent's responses in real Brazilian economic facts instead of training data.

Yes. You can use LlamaIndex's built-in tool filtering to only expose specific tools like `get_variaveis` to certain agents, keeping your context window clean.

Every request passes through a secure, zero-trust connection. The server handles Brazilian census and economic queries inside isolated, ephemeral environments that destroy data upon session termination.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript