Open-Meteo MCP. Global Weather and Air Quality Data on Demand
Open-Meteo provides global weather forecasts and environmental metrics directly through your AI client, requiring zero API keys. Get real-time temperature, multi-day predictions, historical climate data dating back to 1940, comprehensive air quality reports (PM2.5, Ozone), elevation details, and location coordinates—all in one conversation.
Give Claude and any AI agent real-world access
Use a place name to instantly retrieve the precise latitude and longitude needed for all other weather queries.
Pull real-time measurements like temperature, wind speed, humidity, and current weather codes for any location.
Generate detailed forecasts that span multiple days, providing hourly breakdowns of variables up to 16 days out.
Get pollutant levels, including PM2.5, Ozone, and Carbon Monoxide, along with UV index predictions for health planning.
Access archived weather records for a specific location spanning decades, going back to 1940.
Calculate the vertical height of any given set of coordinates, useful for terrain analysis or aviation planning.
Ask an AI about this
Waiting for input…
What AI agents can do with Open-Meteo: Weather & Environment Data (5 Tools)
Use these five tools to query coordinates, predict future weather, check air pollution levels, view historical climate data, and measure ground elevation for any location.
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 MCPGet Air Quality
Retrieves pollutant levels (PM2.5, Ozone, CO) and UV index forecasts for a specific location over up to seven days.
Get Elevation
Calculates the vertical height of any given coordinates, useful for mapping or...
Get Forecast
Provides detailed weather predictions, including temperature and wind speed, for a...
Get Geocoding
Translates a readable place name into usable latitude and longitude coordinates...
Get Historical Weather
Retrieves detailed weather metrics for a location across specific date ranges, going...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Open-Meteo, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Open-Meteo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Getting Location Data Used To Be a Mess.
Before specialized tools like this MCP, gathering location-based data meant juggling three different websites. You'd find the city name on one site, get coordinates from another, and then cross-reference weather metrics or historical pollution levels across a third service. It was constant copy/pasting between spreadsheets and dashboards.
Now, you tell your agent exactly what you need—say, 'What were the air quality conditions in Denver last fall?'—and it handles the entire chain: finding the coordinates, accessing the correct date ranges, pulling the pollutant data (like NO2), and presenting a single, readable answer. You get instant, accurate context.
Open-Meteo MCP delivers reliable weather and air quality metrics.
Manually compiling this kind of data meant dealing with different formats for historical records versus current forecasts. You'd have to check one API endpoint for temperature, another for wind speed, and a third just for the UV index, making any single report incomplete or outdated.
With this MCP, you get all those variables—temperature_2m, uv_index, precipitation, humidity—sourced from one reliable, open-source stream. It’s not just data; it's a complete environmental picture.
What Open-Meteo MCP does for your AI
Need reliable environmental data without the hassle of managing API keys or complex authentication? This MCP connects your AI client directly to Open-Meteo, giving you instant access to global weather patterns and atmospheric measurements. Whether you're tracking air pollution indices for a city or pulling historical temperature logs for climate research, you don't get stuck on setup steps.
You simply ask your agent what you need, and it handles the complex data retrieval. All this information flows through Vinkius, making world-class weather data available to any compatible AI client in plain conversation. It’s pure, open-source environmental intelligence that gets results fast.
019d8464-46ef-7056-948c-49f553f898c0 How to set up Open-Meteo MCP
The bottom line is that you get reliable, complex weather data from open-source sources without ever needing to worry about authentication or API limits.
Subscribe to this MCP in Vinkius. No API key is required; you can start asking questions immediately.
Tell your AI client what location and date range you need data for (e.g., 'What was the air quality in London last June?').
Your agent invokes the necessary tool, fetches the raw environmental metrics, and presents a clear summary back to you.
Who uses Open-Meteo MCP
Environmental researchers and outdoor planners who need deep historical context are the primary users. It's also indispensable for developers building apps that require real-time, reliable location metrics without paying third-party rates.
Running comparative studies by using get_historical_weather to pull specific climate variables from different decades and locations.
Checking the combined forecast—including UV index, wind speed, and precipitation via get_forecast—to ensure an outdoor event is viable for its full duration.
Integrating weather or air quality data into a prototype app by calling tools like get_geocoding first to validate user input coordinates, then using get_air_quality for the payload.
Benefits of connecting Open-Meteo MCP
Access historical data without limits. You can use get_historical_weather to pull climate metrics for any location dating back decades, perfect for academic research.
No authentication headaches. Since this MCP requires no API key, you never worry about rate limits or paying excessive fees just to check the daily forecast.
Comprehensive air quality monitoring. Use get_air_quality to track multiple pollutants—like PM2.5 and Ozone—giving a full picture of local environmental health.
Smart location prep. If you only know the city name, start with get_geocoding. It gives you the precise coordinates needed before running any other tool, preventing errors.
Plan for terrain challenges. Need to know how high an event site is? Use get_elevation to find the ground level at specific coordinates.
Open-Meteo MCP use cases
Planning a Long-Distance Hike
A guide needs to know if the trail will be wet, windy, or too hot. They first use get_geocoding to find the trailhead coordinates, then run get_forecast for the next 7 days to check wind speeds and precipitation patterns.
Analyzing Climate Change Trends
A scientist needs to compare average summer temperatures in Paris across three different decades. They use get_historical_weather, specifying start and end dates for each period to pull the required data points.
Evaluating Urban Pollution Risk
An urban policy analyst wants a full report on air quality for a major industrial zone. They run get_air_quality to check PM10, NO2, and O3 levels, giving them actionable data on pollution hotspots.
Developing an Event Dashboard
A developer needs background metrics for their app. Instead of asking the user for coordinates, they run get_geocoding first to validate a city input and then pass those results directly into get_forecast to populate the dashboard.
Open-Meteo MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Skipping Location Lookup
Trying to analyze current weather or air quality by simply typing 'New York City' into a tool that requires coordinates, leading to an immediate failure because the data format is wrong.
Always start with get_geocoding. It takes plain text (like 'New York City') and outputs the precise latitude/longitude required for subsequent calls to get_forecast or get_air_quality.
Mixing Data Sources
Using a generic search tool that pulls basic weather facts, but misses crucial metrics like pollutant concentration (PM2.5) or elevation data.
Use this MCP because it is specialized. You can run get_air_quality and get_elevation in the same chain to gather a complete environmental profile for any given spot.
Forgetting Time Constraints
Asking for historical weather data without specifying both a start date and an end date, resulting in either no data or only the current day's metrics.
When using get_historical_weather, make sure you provide four inputs: latitude, longitude, a clear start date (YYYY-MM-DD), and a definitive end date.
When to use Open-Meteo MCP
Use this MCP if your primary need is environmental data related to location. This includes weather forecasts, air quality metrics, elevation profiles, or historical climate records. It's the right choice when you are dealing with variables like PM2.5, wind direction, temperature over time, or ground height.
Don't use this if you only need general news updates, stock market prices, or contact information for a business. For those purposes, a dedicated financial or directory tool is better. If your goal is simply to check the current temperature and nothing else, get_forecast works well, but remember that its power comes from its ability to combine multiple complex metrics like air quality via get_air_quality.
Frequently asked questions about Open-Meteo MCP
Does Open-Meteo MCP require an API key? +
No, this MCP does not require any API keys to function. It connects directly to the open-source data stream, meaning you can start querying global forecasts immediately.
How far back can I get historical weather using Open-Meteo? +
The tool supports historical records going all the way back to 1940 for most locations. You just need to use get_historical_weather and define your start and end dates.
What is the difference between get_forecast and get_air_quality? +
get_forecast delivers general weather metrics like temperature, wind speed, and rain chance. get_air_quality focuses specifically on pollutant levels (PM2.5, Ozone) for environmental health reports.
Can I use Open-Meteo MCP to find coordinates? +
Yes, if you only have a city name but need precise coordinates, use get_geocoding first. It gives you the latitude and longitude required for all other environmental tools.
Can this MCP handle multi-variable analysis? +
Absolutely. You can combine multiple tool outputs in one request—for instance, running get_air_quality alongside get_forecast to report both pollution levels and temperature trends for a location.