How to Use the IBGE Nomes — Nomes do Brasil MCP in Google ADK

Q: Can I restrict which IBGE Nomes — Nomes do Brasil tools my Google ADK agent can call?

Use the toolnames filter parameter in your McpToolset configuration. This lets you expose only getnomefrequencia while hiding the ranking and locality tools.

Feed Brazilian demographic census name data directly to your Gemini models via the Google ADK.

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Connect IBGE Nomes — Nomes do Brasil MCP to Google ADK

Create your Vinkius account to connect IBGE Nomes — Nomes do Brasil to Google ADK 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 Nomes — Nomes do Brasil with Google ADK

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Run State-Level Queries in Google ADK

`get_nome_por_localidade` extracts name popularity statistics filtered by Brazilian state codes for your Gemini enterprise agents. This tool feeds clean regional data directly into your Google ADK pipeline to align marketing campaigns with actual local demographics. Gemini's million-token context window processes these state-level tables alongside your BigQuery customer data. You get immediate, localized insights without writing custom data-cleaning scripts.

Analyze Multi-Name Trends Over Decades

`get_nome_frequencia` retrieves birth frequencies from the 1930s onward using piped name strings. Your Google ADK agent parses these decade-by-decade trends to map aging cohorts across Brazil. Combining this MCP Server with Vertex AI lets you build predictive models for regional naming shifts. The tool handles the heavy lifting of pulling census records while your agent synthesizes the historical patterns.

Rank Popular Names with Google ADK MCP Server

`get_ranking_nomes` generates national popularity lists filtered by sex and decade to feed your enterprise analytics. The tool passes clean ranking arrays directly to your Google ADK agent for real-time demographic validation. This MCP integration connects Gemini directly to verified IBGE statistics. Your agents can query these rankings to verify customer personas against actual historical distributions.

Setup guide

Set up IBGE Nomes — Nomes do Brasil MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with IBGE Nomes — Nomes do Brasil tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="IBGE Nomes — Nomes do Brasil_agent",
    model="gemini-2.0-flash",
    instruction="You have access to IBGE Nomes — Nomes do Brasil tools via MCP.",
    tools=mcp_tools,
)

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Common questions about IBGE Nomes — Nomes do Brasil MCP in Google ADK

Instantiate McpToolset with the Vinkius HTTP URL and pass it to your LlmAgent tools parameter. Your Gemini model instantly gains access to the Brazilian census database.

Use the tool_names filter parameter in your McpToolset configuration. This lets you expose only get_nome_frequencia while hiding the ranking and locality tools.

Gemini models process the entire decadal frequency array returned by get_nome_frequencia without truncation. The SDK handles the transport layer so your agent can reason over seventy years of naming data at once.

Yes, the SDK supports both Stdio and HTTP transports. You can test your census queries locally before deploying the agent to Google Cloud.

The server processes only public, aggregated census statistics regarding historical name frequencies. Vinkius runs the query in a secure, ephemeral sandbox that never touches or exposes your internal BigQuery datasets.

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