# Open-Meteo Historical Weather MCP

> Open-Meteo Historical Weather gives you access to 84 years of global climate data, covering everything from temperature and humidity to wind patterns and rainfall. You can pull detailed hourly records or broad daily averages for any location on Earth, making it the ultimate resource for long-term climate research and risk modeling.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Tags:** historical-data, meteorology, climate-science, time-series, api-integration, environmental-data

## Description

This MCP lets your agent access decades of continuous weather history for any place you name. Forget looking up data across multiple physical archives; here you get a single stream of reliable global records dating back to 1940. You can run complex analyses, comparing how rainfall changed between two different decades in the same city or calculating average temperature shifts over fifty years. Whether you're modeling risk for insurance policies, tracking agricultural yield changes, or just curious about historical climate patterns, this MCP handles the heavy lifting. It provides dedicated tools to retrieve general weather metrics across a date range, pull specific daily aggregates like max/min temperatures, and focus purely on long-term temperature trends. Connecting Open-Meteo Historical Weather through Vinkius means your agent has access to one of the largest catalogs of specialized data sources available.

## Tools

### get_historical_weather
Gets comprehensive weather data—including temperature, humidity, wind, and rain—for any date range over 84 years of global records.

### get_historical_daily
Retrieves summarized daily weather reports, providing max/min temperatures, total precipitation, and sunshine duration.

### get_historical_temperature
Focuses on climate trend analysis by retrieving detailed historical data on temperature, apparent temperature, and dewpoint.

## Prompt Examples

**Prompt:** 
```
What was the weather in London on D-Day, June 6, 1944?
```

**Response:** 
```
🏛️ **London — June 6, 1944**

Max temp: 16.2°C | Min: 10.8°C
Precipitation: 2.1mm (light rain)
Wind: 28 km/h from the west
Cloud cover: Heavy overcast

Historical records confirm the famously poor weather conditions that nearly delayed the Normandy invasion.
```

**Prompt:** 
```
Compare average temperatures in São Paulo between 1950 and 2020
```

**Response:** 
```
📊 **São Paulo Temperature Trend (70 years)**

1950s avg: 19.2°C
1980s avg: 19.8°C
2010s avg: 20.6°C

A clear warming trend of +1.4°C over 70 years, consistent with global urban heat island effects.
```

**Prompt:** 
```
How much rain fell in Mumbai during the 2005 flood?
```

**Response:** 
```
🌧️ **Mumbai — July 26, 2005**

Daily precipitation: 944mm (one of the highest single-day totals ever recorded in India)
Hourly peak: 190mm between 14:00-15:00

This catastrophic flood event displaced over 20 million people.
```

## Capabilities

### Calculate historical weather metrics
Retrieve comprehensive hourly and daily climate records (temperature, wind speed, precipitation) for any specified location and date range.

### Analyze long-term temperature trends
Focus on apparent temperature data to model how average temperatures have shifted over decades or centuries at a specific site.

### Generate daily weather summaries
Get aggregated historical records, including maximum and minimum temperatures, total precipitation, and sunshine duration for any given day.

## Use Cases

### Determining flood impact zones
An insurance underwriter asks their agent: 'What was the daily precipitation in Miami between 1980 and 2000?' The agent uses `get_historical_daily` to build a precise risk model, giving the company accurate data for premium setting.

### Assessing agricultural viability
An agronomist needs to compare average growing season temperatures across three different regions over 30 years. The agent uses `get_historical_temperature` to pull longitudinal data, advising the client on climate-resilient crops.

### Investigating historical event conditions
A researcher asks: 'What was the full weather breakdown in London on June 6, 1944?' The agent uses `get_historical_weather` to retrieve detailed hourly data for that specific date.

### Comparing climate shifts
A developer wants to show clients how much cooler summers used to be. They use the MCP, specifically targeting temperature trends across multiple coordinates over a 70-year period.

## Benefits

- Instead of guessing, you get hard data. Using `get_historical_weather`, your agent pulls comprehensive records for any date range, letting you pinpoint exact historical conditions.
- Model risk with certainty. By using `get_historical_daily`, you move beyond simple averages to access specific daily aggregates like max/min temperatures and total precipitation.
- Track long-term warming trends efficiently. The dedicated tool, `get_historical_temperature`, focuses purely on apparent temperature data, perfect for climate trend analysis.
- Avoid manual cross-referencing. This MCP consolidates decades of global weather history into a single source accessible by your agent.
- Support complex modeling needs. It handles coordinates and date ranges globally, supporting everything from local farm planning to continental risk assessment.

## How It Works

The bottom line is that your agent processes global climate archives into clean, actionable data sets.

1. Specify the target location by providing latitude and longitude coordinates.
2. Define the time window you need data for (start date and end date) and select whether you need general, daily, or trend-specific metrics.
3. Your agent pulls the historical records into a structured format ready for immediate analysis.

## Frequently Asked Questions

**How far back can I go with Open-Meteo Historical Weather?**
The MCP covers 84 years of continuous records, going back to 1940. This range is suitable for nearly any long-term climate study.

**Do I need coordinates or just city names for get_historical_weather?**
You must provide the exact latitude and longitude coordinates for all historical queries to ensure accurate data retrieval. City names aren't specific enough.

**What difference is there between get_historical_daily and get_historical_temperature?**
Daily retrieves general aggregates like total precipitation and max/min temps for a day. Temperature focuses specifically on apparent temperature data, which is better for long-term climate trend analysis.

**Can I compare two different cities using Open-Meteo Historical Weather?**
Yes. You simply run separate queries for the coordinates of each city and then use your agent to synthesize the resulting time series data into a single comparison report.

**Is the weather data in get_historical_weather hourly or daily?**
Depending on the parameters you provide, `get_historical_weather` can deliver both comprehensive hourly records and broader daily summaries.