# INMET Weather MCP

> INMET (Apitempo - Meteorologia) MCP provides direct access to Brazil's official meteorological data from INMET. Use this MCP to query historical and real-time atmospheric measurements, fetch detailed city forecasts using IBGE codes, or analyze regional weather patterns across the country. It also gives you URLs for the latest GOES-16 satellite imagery metadata. Ideal for anyone needing precise climate intelligence.

## Overview
- **Category:** government-public-data
- **Price:** Free
- **Tags:** meteorology, brazil-weather, weather-forecast, satellite-imagery, climate-data

## Description

Need to know what's happening with the weather in Brazil? This connector lets your AI client pull official data straight from INMET, bypassing messy manual API calls. You can query specific stations—whether they're automatic or manually monitored—to get hourly or daily measurements like temperature and pressure. Want a regional view? Just specify an area, and the MCP gathers all station data points for that region on a given date. It handles forecasts too; you don't need to guess which city code to use. Plus, it gives you metadata links for GOES-16 satellite imagery, so your agent can pull visual context alongside numerical reports. Connecting this via Vinkius makes sure all these complex data sources are available through a single point in your AI workflow.

## Tools

### get_all_forecasts
Retrieves weather predictions for every supported city in Brazil.

### get_forecast_by_city
Gets the detailed weather forecast when you specify a single city's code or name.

### get_meteorological_data_by_date
Pulls daily measurements for a specific monitoring station ID on a given date.

### get_meteorological_data_by_region
Collects data from every station within a defined Brazilian region for a single day.

### get_satellite_images
Fetches links to the newest GOES-16 satellite imagery metadata available for review.

### get_station_data_daily
Retrieves comprehensive daily weather metrics for one specific station ID.

### get_station_data_hourly
Grabs highly detailed hourly readings (temp, pressure) for a single station and time period.

### list_stations
Lists all available meteorological stations across Brazil, letting you identify the correct monitoring point by type.

## Prompt Examples

**Prompt:** 
```
List all automatic weather stations in Brazil.
```

**Response:** 
```
I've retrieved the list of automatic stations. There are hundreds across Brazil, such as A001 (Brasília) and A601 (Rio de Janeiro). Would you like to see data for a specific station ID?
```

**Prompt:** 
```
What is the weather forecast for city code 3304557?
```

**Response:** 
```
Fetching forecast for Rio de Janeiro (3304557)... The forecast indicates clear skies with a maximum of 32°C and a minimum of 22°C for today. Would you like the hourly breakdown?
```

**Prompt:** 
```
Show me the latest satellite images from GOES-16.
```

**Response:** 
```
I've accessed the latest GOES-16 satellite data. I have several URLs for different channels (Infrared, Water Vapor, Visible). Which one would you like to view?
```

## Capabilities

### Get General Weather Forecasts
Fetch current and predicted weather conditions for multiple cities across Brazil.

### Analyze Station History
Retrieve hourly or daily measurements (temp, humidity) for specific monitoring stations over time.

### Map Regional Data Sets
Query and aggregate all available weather readings for a defined geographical region on a single date.

### Access Satellite Imagery Links
Pull the latest GOES-16 satellite metadata, giving you direct URLs for visual monitoring tools.

## Use Cases

### Planning for Disaster Relief
A field manager needs to know the expected weather across three different states over the next 72 hours. They use `get_meteorological_data_by_region` multiple times, followed by `get_all_forecasts` to build a complete, actionable picture for resource allocation.

### Researching Historical Drought Patterns
A climate researcher needs to compare humidity levels across the Northeast region from 2018 versus 2023. They use `get_meteorological_data_by_region` for both years, allowing them to analyze historical variations in a structured way.

### Optimizing Farming Schedules
A farm manager must determine the best day for planting based on rain predictions. They use `get_forecast_by_city` and then verify the expected conditions over the next week using `get_station_data_daily` from a local station.

### Validating Equipment Needs
An engineer needs to know if an outdoor site can handle high winds. They check the latest GOES-16 data via `get_satellite_images`, then use `get_station_data_hourly` to find recent peak wind speed readings for validation.

## Benefits

- You stop guessing which API endpoint to hit. Instead, you use `list_stations` first. This gives your agent a definitive list of all automatic and manual stations before requesting any data, guaranteeing accuracy.
- The MCP handles time complexity. You don't write separate logic for hourly vs. daily data; whether you need the granular details from `get_station_data_hourly` or an overview from `get_meteorological_data_by_date`, the tool call is straightforward.
- It links visual and numerical data. Beyond just numbers, you can use `get_satellite_images` to pull GOES-16 metadata URLs. This allows your agent to correlate visible cloud cover with measured atmospheric pressure changes.
- Regional analysis becomes a single step. Instead of running queries for dozens of individual station IDs, calling `get_meteorological_data_by_region` aggregates all necessary data points for the entire area you care about.
- Forecasts are comprehensive. You can get immediate predictions for an entire region using `get_all_forecasts`, or narrow it down precisely to a single city's predicted conditions with `get_forecast_by_city`.

## How It Works

The bottom line is your AI client gets standardized access to complex, government-grade weather datasets without writing custom integration code.

1. First, tell your agent if you need general forecasts or historical readings. You might start by listing stations to find the correct ID.
2. Next, provide the required parameters: a date range for history, a specific city code for forecasts, or a region name for regional analysis.
3. The MCP executes the query and returns structured data packages containing measurements, forecast details, or image metadata URLs.

## Frequently Asked Questions

**How do I find out which station IDs are valid in Brazil? (list_stations)**
Run `list_stations` first. This tool compiles a list of all automatic and manual stations across the country, letting you verify correct identifiers before attempting to pull data.

**Can I get hourly temperature changes for a specific location? (get_station_data_hourly)**
Yes. Use `get_station_data_hourly`. You just need the station ID and the time window, and it returns granular readings like temperature and pressure hour-by-hour.

**Is there a tool to get forecasts for all cities at once? (get_all_forecasts)**
Yes, `get_all_forecasts` retrieves weather predictions for every supported city. This is faster than calling `get_forecast_by_city` dozens of times.

**What if I need to analyze data from a specific time period last year? (get_meteorological_data_by_date)**
Use `get_meteorological_data_by_date`. You must provide the station ID and the exact date you want measurements for. This keeps your historical analysis focused.

**How does using `get_meteorological_data_by_region` help when I need to compare weather across several adjacent Brazilian areas?**
It gathers data for every station within the specified region. You give it a predefined group code, and it pulls all relevant measurements (temperature, humidity, pressure) for that area on your chosen date.

**What kind of information do I get when I call `get_satellite_images`?**
You access the latest GOES-16 satellite data. This tool returns metadata and direct URLs, allowing you to view visual weather monitoring channels like infrared or visible spectrums.

**If I only need daily averages instead of hourly readings, is `get_station_data_daily` the right tool?**
Yes, use `get_station_data_daily`. While `get_station_data_hourly` gives granular measurements, this function provides aggregated or averaged data for a specific station across an entire 24-hour period.

**How do I get a precise forecast just for one city using the `get_forecast_by_city` tool?**
This tool focuses the retrieval on a single location. You supply the unique IBGE code or city identifier, and your agent immediately fetches the detailed weather forecast for that specific municipality.

**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.