How to Use the Correios do Brazil MCP in Pydantic AI

Use Pydantic AI to enforce strict type-safety on every Correios do Brazil postal request.

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Connect Correios do Brazil MCP to Pydantic AI

Create your Vinkius account to connect Correios do Brazil 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 Correios do Brazil with Pydantic AI

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Type-safe postal pricing in Pydantic AI

The `calculate_prices` tool returns structured data that your agent validates against Pydantic models. If the API format changes, your agent stops immediately to prevent bad data from hitting your systems. This approach guarantees that your shipping calculations are always typed correctly. You don't have to worry about silent errors or unexpected field types.

Validate ZIP codes in Pydantic AI

Your agent uses `consult_cep` to pull address info, with every response checked by your models. This ensures your customer database stays consistent and bug-free. It forces the agent to handle the data exactly as defined. You get reliable address records that match your internal schema every single time.

Reliable delivery estimates for Pydantic AI

The `estimate_delivery_time` tool provides arrival data that gets validated at runtime. Your agent rejects any malformed responses before they reach your logistics dashboard. This keeps your application logic clean and predictable. Your agent only acts on data that strictly conforms to your pre-defined shipping models.

Setup guide

Set up Correios do Brazil 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": {
        "correios-do-brazil-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Correios do Brazil transactions")
print(result.output)

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Common questions about Correios do Brazil MCP in Pydantic AI

You define your Pydantic models for the tool outputs, and the framework validates the JSON returned by the server. If the data doesn't match your schema, the agent raises a validation error.

The agent will fail loudly with a validation error rather than guessing what the data means. This prevents corrupted information from causing issues in your downstream systems.

Yes, it supports both Streamable HTTP and SSE transports. You simply pass the endpoint to your toolset, and the framework manages the connection.

Since the framework is model-agnostic, you can pair the postal server with any local model. The validation logic remains the same regardless of the model provider.

The server only touches public ZIP code data, which carries no privacy risk. Your Pydantic models further ensure that only the expected geographic fields are parsed and stored.

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