Open-Meteo Historical Weather MCP. Analyze 84 Years of Global Climate Data
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.
Give Claude and any AI agent real-world access
Retrieve comprehensive hourly and daily climate records (temperature, wind speed, precipitation) for any specified location and date range.
Focus on apparent temperature data to model how average temperatures have shifted over decades or centuries at a specific site.
Get aggregated historical records, including maximum and minimum temperatures, total precipitation, and sunshine duration for any given day.
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What AI agents can do with Open-Meteo Historical Weather: 3 Tools
Use these three specialized tools to retrieve comprehensive historical weather metrics, from general records to dedicated temperature trend analysis.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Open-Meteo Historical Weather MCPGet 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...
Get Historical Temperature
Focuses on climate trend analysis by retrieving detailed historical data on...
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Manually tracking historical climate data is a nightmare of clicks.
Today, analyzing long-term weather shifts means opening multiple academic databases. You're clicking through reports for rainfall totals on one tab, max temperatures on another, and then cross-referencing wind speed from a third source—all while copy-pasting dates and coordinates into a spreadsheet.
With this MCP, the process is simple: you ask your agent for the data range and type of metric. It pulls everything together automatically. You get clean, structured climate records ready for modeling in minutes.
Open-Meteo Historical Weather gives you the full picture.
You no longer have to worry about whether a single source provides hourly data or only daily aggregates. The MCP handles general records with `get_historical_weather`, dedicated summaries via `get_historical_daily`, and specialized trend metrics using `get_historical_temperature`.
Your workflow shifts from spending hours gathering fragmented data points to actually analyzing the patterns that matter.
What Open-Meteo Historical Weather MCP does for your AI
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.
019d75e8-2ba7-71ca-b449-3d5ba7ffe462 How to set up Open-Meteo Historical Weather MCP
The bottom line is that your agent processes global climate archives into clean, actionable data sets.
Specify the target location by providing latitude and longitude coordinates.
Define the time window you need data for (start date and end date) and select whether you need general, daily, or trend-specific metrics.
Your agent pulls the historical records into a structured format ready for immediate analysis.
Who uses Open-Meteo Historical Weather MCP
Anyone who deals with risk over time—from insurance adjusters needing to check flood records years ago to agronomists predicting yield changes. If your job involves tracking patterns that span more than a season, you need this.
Uses the MCP to compare average temperature shifts between different decades or analyze precipitation changes across continents.
Checks historical rainfall and extreme weather data for specific coordinates to advise on optimal planting seasons or crop selection.
Retrieves long-term daily aggregates of flood and storm damage metrics to accurately price regional risk models.
Benefits of connecting Open-Meteo Historical Weather MCP
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.
Open-Meteo Historical Weather MCP 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.
Open-Meteo Historical Weather MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Using general search engines
Searching Google for 'weather data in Paris 1955' yields dozens of academic PDFs and fragmented, unstandardized datasets that require manual cleaning.
Use the get_historical_weather tool within your agent. It takes coordinates and date ranges directly, delivering clean, structured historical records instantly.
Relying on single-source weather APIs
Using a service that only provides current or seasonal data means you can't compare 1940 to today; your analysis is incomplete.
This MCP accesses global archives spanning over eight decades. Use get_historical_daily to ensure you capture the necessary long-term historical scope.
Confusing daily averages with trends
Treating a single day's temperature reading as representative of an entire decade leads to major modeling errors.
For trend analysis, use get_historical_temperature. It is designed specifically to isolate and track apparent temperature shifts over extended periods.
When to use Open-Meteo Historical Weather MCP
Use this MCP if your project requires time-series data spanning multiple decades or centuries. You need concrete metrics like precipitation totals, wind patterns, or temperature averages across specific coordinates for a defined range of dates. Don't use it if you only care about today’s forecast; those tools are designed for the past.
If you only need to check the weather for one random day in the last five years, get_historical_weather works fine. But if your core task is calculating the rate of warming or comparing climate stability across different time periods (e.g., 1950 vs. 2020), you must use the specialized tools like get_historical_temperature and get_historical_daily. This MCP is for deep, longitudinal research, not quick checks.
Frequently asked questions about Open-Meteo Historical Weather MCP
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.