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

Use Pydantic AI to validate IBGE Censo & SIDRA — Dados Censitários data with type-safe models.

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Connect IBGE Censo & SIDRA — Dados Censitários MCP to Pydantic AI

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

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Perplexity

Type-safe census data ingestion

Every response from `get_agregado_data` is validated against your Pydantic models. If the API structure changes, your agent fails immediately rather than processing corrupt numbers. This provides a safety layer for your economic models. You define the schema, and the server ensures the data matches, preventing silent failures in your analysis pipeline.

Strict metadata validation

Use `get_agregado_metadados` to define expected variable types before querying the database. Your agent verifies the structure of the data before it even attempts to pull the final figures. This approach eliminates hallucinated fields. By enforcing strict types, you make your agent's interaction with the IBGE API predictable and repeatable.

Reliable aggregate discovery

Query `list_agregados` and `get_agregado_periodos` to identify valid data paths for your agent. Pydantic AI enforces that these inputs are correctly formatted before the request reaches the server. Your logic stays clean and focused. By treating MCP responses as typed objects, you remove the need for manual parsing and error checking in your agent code.

Setup guide

Set up IBGE Censo & SIDRA — Dados Censitários 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-censo-sidra-dados-censitarios-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to IBGE Censo & SIDRA — Dados Censitários tools.",
)

result = await agent.run("List recent IBGE Censo & SIDRA — Dados Censitários transactions")
print(result.output)

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Common questions about IBGE Censo & SIDRA — Dados Censitários MCP in Pydantic AI

It ensures that every piece of data retrieved from the SIDRA API matches your expected Python data classes. This prevents runtime errors caused by unexpected API responses.

Install the slim package, define your MCPToolset with the server URL, and pass it to your agent. The framework handles the connection and validation automatically.

Yes. The framework supports both Streamable HTTP and SSE transports, allowing you to choose the best connection method for your specific production environment.

The agent will raise a validation error immediately. This stops the execution flow, forcing a clean exit instead of letting the agent proceed with incorrect census values.

The server processes only public statistical aggregates. Your Pydantic models act as a local security boundary, ensuring that only the specific, validated data you requested enters your agent's memory.

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