Bring Meteorology
to Pydantic AI
Learn how to connect INMET (Apitempo - Meteorologia) to Pydantic AI and start using 8 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the INMET (Apitempo - Meteorologia) MCP Server?
Connect to the INMET (Instituto Nacional de Meteorologia) API to retrieve comprehensive weather data across Brazil. This server allows AI agents to query a vast network of automatic and manual stations, providing precise atmospheric measurements and forecasts.
What you can do
- Meteorological Stations — List all automatic (T) and manual (M) stations across the Brazilian territory.
- Historical & Real-time Data — Fetch daily or hourly measurements (temperature, humidity, pressure) for specific station IDs.
- Regional Analysis — Query data for all stations within specific Brazilian regions (N, NE, CO, SE, S) for a given date.
- Weather Forecasts — Get detailed forecasts for cities using IBGE codes or retrieve all available forecasts at once.
- Satellite Imagery — Access the latest GOES-16 satellite metadata and image URLs for visual weather monitoring.
How it works
- Subscribe to this server
- Enter your INMET API Token (if required)
- Start querying weather patterns directly from your AI assistant
Who is this for?
- Data Scientists & Researchers — analyze climate trends and historical weather patterns in Brazil.
- Logistics & Agriculture — monitor local weather conditions and forecasts to optimize operations.
- Developers — integrate official Brazilian meteorological data into applications without complex API handling.
Built-in capabilities (8)
Get weather forecasts for all supported cities
Get weather forecast for a specific city
Get meteorological data by date for a station
Get meteorological data for all stations in a specific region
Get latest GOES-16 satellite images
Get daily meteorological data for a specific station
Get hourly data for a specific station and time
List meteorological stations by type
Why Pydantic AI?
Pydantic AI validates every INMET (Apitempo - Meteorologia) tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your INMET (Apitempo - Meteorologia) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your INMET (Apitempo - Meteorologia) connection logic from agent behavior for testable, maintainable code
INMET (Apitempo - Meteorologia) in Pydantic AI
INMET (Apitempo - Meteorologia) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect INMET (Apitempo - Meteorologia) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for INMET (Apitempo - Meteorologia) in Pydantic AI
The INMET (Apitempo - Meteorologia) MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 8 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
INMET (Apitempo - Meteorologia) for Pydantic AI
Every tool call from Pydantic AI to the INMET (Apitempo - Meteorologia) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I get the weather forecast for a specific city in Brazil?
You can use the get_forecast_by_city tool by providing the city's IBGE code. The agent will return detailed forecast information including temperature and conditions.
Can I access real-time satellite imagery of Brazil?
Yes! Use the get_satellite_images tool to retrieve the latest metadata and URLs for GOES-16 satellite images covering the Brazilian territory.
How do I find the ID of a meteorological station?
Use the list_stations tool with the type 'T' for automatic or 'M' for manual stations. This will provide a list of all stations and their respective IDs.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your INMET (Apitempo - Meteorologia) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
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Update: pip install --upgrade pydantic-ai
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