# Meteostat MCP for AI Agents AI Agent Connect

> Meteostat lets your AI agent pull historical weather data and climate statistics from thousands of stations worldwide. It handles everything from hourly observations to 30-year climate averages. Whether you're looking for specific city records or need to interpolate data for a remote coordinate, this Connector connects your agent to one of the most extensive meteorological databases available.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_JKO90Yyhey42dI7p2Gfm0lxPKzXASmcXKPATx7N7/ai-agent-connect
- **Tags:** weather-data, historical-weather, climate-statistics, meteorology, geospatial

## Description

Imagine you're trying to figure out the average rainfall in a specific region over the last decade, or you need to know the exact temperature fluctuations in a city during a specific week in 2018. Usually, this involves hunting through various government websites, downloading messy CSVs, and trying to clean up the data yourself. With this Connector, you just ask your agent to do the heavy lifting. It pulls the raw numbers directly into your conversation, whether you're looking for precise station records or need to estimate weather for a spot that doesn't have a nearby station. It's a huge time saver for anyone who needs climate context without the manual data entry. You can find this and thousands of other tools in the Vinkius catalog to build out your agent's capabilities. It takes the guesswork out of meteorological research by giving your agent a direct line to high-quality, verified data. You get clean records for your models or reports instantly without the headache of manual scraping.

## Tools

### point_monthly
Get historical monthly data for a specific coordinate. Use this when you need a monthly trend for a city without a station.

### point_normals
Get 30-year climate normals for a point. This is perfect for understanding regional climate baselines.

### stations_daily
Fetch daily statistics for a specific station. You can request up to 10 years of data at once.

### stations_meta
Get metadata for a station using its ID, WMO, or ICAO code. Use this to identify specific stations in a region.

### stations_monthly
Get historical monthly statistics for a specific station. This helps you see seasonal changes over several years.

### stations_nearby
Find weather stations based on GPS coordinates and a radius. Use this to find the closest reliable data source.

### stations_hourly
Pull hourly observations for a specific station. This is best for high-resolution data over a 30-day period.

### stations_normals
Get the 30-year climate normals for a specific station. This provides a clear view of long-term weather averages.

### point_daily
Get historical daily data for a specific coordinate. This works well for locations where no station exists nearby.

### point_hourly
Get historical hourly data for a specific coordinate. Use this for precise hourly changes at any location on Earth.

## Prompt Examples

**Prompt:** 
```
What was the average daily temperature in Tokyo for the month of July in 2021?
```

**Response:** 
```
### July 2021 Daily Temperatures - Tokyo

| Date | Avg Temp (°C) |
| :--- | :--- |
| July 1 | 28.4 |
| July 2 | 29.1 |
| July 3 | 30.5 |
| ... | ... |

**Summary:**
- **Warmest Day:** July 15 (33.2°C)
- **Coolest Day:** July 1 (28.4°C)
- **Monthly Average:** 29.8°C
```

**Prompt:** 
```
Find the nearest weather stations to the Grand Canyon (36.05, -112.17).
```

**Response:** 
```
I found the following stations near the Grand Canyon:

- **Station A** (ID: 12345) - 12.4 km away
- **Station B** (ID: 67890) - 25.1 km away
- **Station C** (ID: 11223) - 38.8 km away

Would you like me to pull the historical records for one of these?
```

**Prompt:** 
```
What are the 30-year climate normals for a station in Berlin?
```

**Response:** 
```
### Climate Normals: Berlin (30-Year Average)

- **Annual Mean Temp:** 10.2°C
- **Precipitation:** 570 mm
- **Seasonal Breakdown:**
  - **Winter:** 3.5°C
  - **Spring:** 12.1°C
  - **Summer:** 19.8°C
  - **Autumn:** 13.4°C

*Note: These values represent the long-term baseline for the region.*
```

## Capabilities

### Find nearby weather stations
Locate the closest weather stations to any GPS coordinate within a specific radius.

### Fetch historical observations
Get hourly, daily, or monthly weather records for specific stations.

### Estimate weather for any point
Retrieve weather data for any coordinate on Earth, even without a local station.

### Access 30-year climate normals
Pull long-term climate averages to understand regional weather baselines.

### Retrieve station metadata
Get detailed information like WMO and ICAO identifiers for specific stations.

## Use Cases

### Climate Modeling
A researcher needs to feed 10 years of daily temperature data into a model. They ask the agent to pull data for a specific region using stations_daily.

### Logistics Planning
A freight company wants to know the weather patterns for a specific route in 2022. They use point_monthly to see the trend.

### Real Estate Analysis
An analyst wants to know if a city is getting hotter over time. They use point_normals to compare 30-year averages.

### App Development
A dev wants to add historical weather context to a dashboard. They use stations_nearby to find the best local data.

## Benefits

- Skip the manual CSV downloads by using stations_daily to get up to 10 years of data in one go.
- Analyze remote areas accurately with point_hourly and point_daily for locations without nearby stations.
- Establish reliable climate baselines using stations_normals to see 30-year averages for any station.
- Identify the best data sources in a region quickly with stations_nearby based on your exact coordinates.
- Get specific station details like ICAO or WMO identifiers using stations_meta for precise research.

## How It Works

The bottom line is your AI becomes a meteorology expert with instant access to global weather history.

1. Subscribe to the Meteostat MCP and get your RapidAPI key.
2. Connect the Connector to your AI client like Claude or Cursor.
3. Ask your agent to pull specific weather records or climate trends for any location.

## Frequently Asked Questions

**Can I get weather data for a place that doesn't have a weather station with the Meteostat MCP?**
Yes. You can use point-based data which interpolates weather information for any specific coordinate on Earth, even in remote areas.

**How far back does the historical data go with the Meteostat MCP?**
The data depth depends on the specific station, but it often covers several decades of daily and monthly records.

**Does the Meteostat MCP provide live weather updates?**
No, this Connector is strictly for historical weather data and climate statistics. It does not provide real-time forecasts or current conditions.

**What is the difference between daily and hourly data in the Meteostat MCP?**
Hourly data provides a high-resolution look at temperature and precipitation changes throughout a single day, while daily data gives you a single summarized record for the 24-hour period.

**Can I use the Meteostat MCP for commercial research projects?**
Yes, you can use it to pull data for commercial analysis, though you should review the Meteostat terms of service regarding specific data redistribution.

**How can I find weather data for a location that doesn't have a specific weather station?**
You can use the `point_hourly` or `point_daily` tools. These tools use interpolation to calculate weather data for any geographic coordinate (latitude/longitude) by combining data from surrounding stations.

**What is the difference between historical data and climate normals?**
Historical tools like `stations_daily` provide actual observations for specific dates. The `stations_normals` tool provides long-term statistical averages (usually over 30 years), which represent the 'typical' weather for a location.

**Can I get weather data in Fahrenheit instead of Celsius?**
Yes. Most tools, such as `stations_hourly` and `point_daily`, include an optional `units` parameter. You can set this to 'imperial' to receive data in Fahrenheit and other non-metric units.