How to Use the INMET (Apitempo - Meteorologia) MCP in Pydantic AI

Q: Does Pydantic AI validate the output of INMET (Apitempo - Meteorologia) automatically?

Yes, the framework wraps tools like getmeteorologicaldatabyregion in Pydantic models. Any response that violates your schema triggers a validation error, preventing silent failures.

Run type-safe Brazilian weather queries in Pydantic AI with strict runtime validation and zero silent data corruption.

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Connect INMET (Apitempo - Meteorologia) MCP to Pydantic AI

Create your Vinkius account to connect INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) with Pydantic AI

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Validate INMET meteorological data with strict runtime schemas

The `get_station_data_hourly` tool retrieves raw atmospheric readings that are immediately validated against Pydantic models. If the Brazilian government API returns an unexpected null value or a malformed float, Pydantic AI halts execution immediately. This strict verification prevents corrupt data from contaminating your downstream application logic. You can confidently pass telemetry from `get_station_data_daily` to your core systems, knowing every field matches your exact type definitions.

Prevent model hallucinations using type-safe MCP Server tools

The `get_forecast_by_city` tool maps weather predictions to structured Python objects. By forcing your model to interact with this MCP Server through Pydantic schemas, you eliminate the risk of the model inventing weather metrics or station IDs. The model cannot hallucinate a temperature reading when the output is bound to a strict float field. If the model attempts to return invalid data, the framework rejects the response and forces a correction loop.

Parse complex satellite metadata with zero manual mapping

The `get_satellite_images` tool returns raw image metadata from the GOES-16 satellite. Pydantic AI automatically parses these payloads into clean, typed Python dictionaries, saving you from writing custom JSON parsers. Your agent can immediately extract image URLs and timestamps to determine cloud cover trends. If the API structure changes, your application fails loudly at the boundary, allowing you to catch integration issues before they hit production.

Setup guide

Set up INMET (Apitempo - Meteorologia) 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": {
        "inmet-apitempo-meteorologia-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent INMET (Apitempo - Meteorologia) transactions")
print(result.output)

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Common questions about INMET (Apitempo - Meteorologia) MCP in Pydantic AI

Instantiate MCPToolset with your Vinkius HTTP endpoint URL. Pass this toolset into your Agent constructor to give your model immediate access to endpoints like list_stations.

Yes, the framework wraps tools like get_meteorological_data_by_region in Pydantic models. Any response that violates your schema triggers a validation error, preventing silent failures.

Yes, Pydantic AI is model-agnostic. You can connect this MCP Server to local models or commercial APIs while maintaining the exact same type-safety guarantees across all endpoints.

You should avoid using MCPServerHTTP, which is now deprecated. Use the unified MCPToolset class instead to handle all HTTP and SSE transport connections.

Your requests for meteorological data are routed through a sandboxed V8 runtime. All credentials and telemetry parameters are wiped from memory immediately after the tool execution completes.

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