How to Use the IBGE Serviços de Dados MCP in Pydantic AI

Build type-safe, validated pipelines using Pydantic AI and official IBGE Serviços de Dados datasets.

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Connect IBGE Serviços de Dados MCP to Pydantic AI

Create your Vinkius account to connect IBGE Serviços de Dados 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 Serviços de Dados with Pydantic AI

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Claude

Perplexity

Type-safe metadata parsing for Pydantic AI

Stop guessing what the API returns. Use `get_metadados` to define your structures, and Pydantic AI will validate every field at runtime. If the IBGE schema changes, your agent crashes safely instead of processing corrupted data. This keeps your pipeline reliable and predictable.

Geographic lookup validation in Pydantic AI

Look up precise geographic names using `get_nome_geografico` with full type checking. Your agent receives verified, clean objects rather than loose dictionaries. This makes your code cleaner and easier to debug. You know exactly what data you are working with at every step of the agent's decision tree.

Rigorous data integration for Pydantic AI

Force your agent to respect strict schema definitions when pulling census data. By wrapping `get_agregados` in a Pydantic-validated toolset, you ensure data integrity. This is the most reliable way to handle external statistics. You get immediate feedback if the data structure doesn't match your expectations.

Setup guide

Set up IBGE Serviços de Dados 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-servicos-de-dados-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent IBGE Serviços de Dados transactions")
print(result.output)

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Common questions about IBGE Serviços de Dados MCP in Pydantic AI

Pydantic AI compares the raw JSON response against your predefined models. If a field is missing or the wrong type, the agent raises a validation error immediately.

Yes, the framework is model-agnostic. You can run your local model and still get the benefit of Pydantic validation on the MCP responses.

Install the slim package, initialize the MCPToolset with your Vinkius endpoint, and pass it to your agent constructor. It handles the transport layer automatically.

The MCP transport will time out or return a connection error. Your agent should handle these as standard operational failures.

The server runs in an isolated V8 sandbox. No persistence layer exists, and your agent only receives the specific data returned by the requested tools.

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