How to Use the IBGE Nomes MCP in Pydantic AI

Use Pydantic AI to enforce strict type-safety on your Brazilian demographic data queries.

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Connect IBGE Nomes MCP to Pydantic AI

Create your Vinkius account to connect IBGE Nomes to Pydantic AI 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 with Pydantic AI

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Type-safe frequency checks in Pydantic AI

Your agent uses `get_name_frequency` to fetch birth counts, and Pydantic AI validates every response against your models. If the data format drifts, your agent catches it immediately. This prevents silent failures during data ingestion. You get reliable, typed structures for every name frequency query you run.

Validated name rankings for Pydantic AI

The `get_names_ranking` tool returns data that your agent validates at runtime. You ensure your agent only acts on confirmed demographic rankings that meet your strict schema requirements. This approach eliminates hallucinated fields or unexpected data types. It provides a foundation for building agents that prioritize data correctness over everything else.

Standardized MCP toolsets

You integrate this MCP server using the unified MCPToolset in Pydantic AI. It handles the communication between your agent and the IBGE source with built-in validation. Your agent maintains a clear, typed contract with the data. This allows you to build complex demographic logic without worrying about inconsistent API responses.

Setup guide

Set up IBGE Nomes MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "ibge-nomes-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to IBGE Nomes tools.",
)

result = await agent.run("List recent IBGE Nomes transactions")
print(result.output)

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Common questions about IBGE Nomes MCP in Pydantic AI

Pydantic AI validates the tool outputs against your defined models at runtime. Any response that doesn't match your schema triggers an immediate validation error.

Yes. The framework is model-agnostic, meaning you use the same IBGE Nomes toolset with OpenAI, Anthropic, or local models.

Yes. The server supports SSE and Streamable HTTP, giving you flexibility in how your Pydantic AI agent communicates with the source.

Your Pydantic AI agent will fail loudly with a validation error. This prevents your application from processing corrupt or unexpected demographic data.

No. The tools only return public birth statistics. Your agent processes these aggregate numbers without touching any private individual identifiers.

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