AerisWeather MCP for AI. Get immediate weather data for any place on Earth.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
AerisWeather lets your AI agent access professional, global weather intelligence. Query real-time observations, 15-day forecasts, and official warnings for any location worldwide.
You can check current conditions using `get_observations`, map out coordinates with `get_places`, or pull historical data to run reports—all through natural conversation.
What your AI can do
Get alerts
Retrieves official warnings and advisories for active weather dangers in a region.
Get batch
Allows you to query several data types, like observations and forecasts, in one efficient call.
Get conditions
Provides global current, forecast, or historical weather conditions, including precipitation details.
Gets immediate, real-time conditions (METAR/PWS) for specific airports or cities.
Generates detailed forecasts spanning multiple days and can check minute-by-minute precipitation trends.
Checks authoritative sources for active watches, advisories, and severe storm alerts in a given area.
Pulls necessary geographical context like time zones, coordinates, or population size for any place name.
Combines multiple weather requests (like forecasts and alerts) into a single API call to save cycles and speed up the process.
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AerisWeather with 6 Tools
Use these tools to retrieve everything from live conditions and historical data to geographical coordinates for any location on Earth.
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 AerisWeather on VinkiusGet Alerts
Retrieves official warnings and advisories for active weather dangers in a region.
Get Batch
Allows you to query several data types, like observations and forecasts, in one...
Get Conditions
Provides global current, forecast, or historical weather conditions, including...
Get Forecasts
Generates general weather outlooks for specific locations over an extended period.
Get Observations
Pulls the absolute latest, real-time atmospheric data from measured sites...
Get Places
Gathers basic geographical facts about a location, such as its time zone or population count.
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 every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AerisWeather, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AerisWeather. 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.
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No stored credentials
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Policy on every call
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually checking weather data across multiple dashboards is a huge time sink.
Today, coordinating operations means jumping through hoops: you check one site for current conditions, another for 15-day forecasts, and a third dedicated portal just to see if there are active warnings. It's click-heavy; it takes minutes of copy-pasting data points into spreadsheets just to build a basic risk assessment.
With this MCP, you tell your agent the job—e.g., 'Assess the readiness for deployment in Tampa.' The system runs `get_observations`, checks for warnings with `get_alerts`, and generates the whole report for you. You get actionable intelligence without touching a browser.
Get precise, verifiable weather context using AerisWeather.
You no longer need to open multiple tabs or switch between specialized meteorological sites just to cross-reference data. The agent handles the calls; it pulls everything—from historical trends via `get_conditions` to immediate status updates with `get_observations`. It’s one unified stream of information.
The difference is speed and accuracy. You get a complete, verifiable picture of a location's environment in seconds, not an hour.
What your AI can actually do with this
Getting reliable weather context used to mean opening five different websites just to piece together a picture: one for the forecast, another for alerts, and yet another for geographical details. This MCP changes that. You connect it once via Vinkius, and your AI client handles all the legwork. It pulls real-time METAR data, generates multi-day forecasts, or checks if there are active flood warnings—all in one conversation.
It’s about getting precise context quickly. Instead of scrolling through dozens of charts, you ask for specific data points: 'What's the precipitation rate in Miami next week?' The agent uses this MCP to find that answer instantly, giving you actionable intelligence whether you're planning a logistics route or running an environmental model.
019e5cf7-d93b-7059-a04e-4c2c7ecd678e Here's how it actually works
The bottom line is that it lets your AI client access professional-grade weather context without you writing any code.
First, subscribe to this MCP on Vinkius and enter your AerisWeather Client ID and Secret.
Next, tell your AI client exactly what you need—for example: 'What's the weather forecast for New Orleans next week?'
The agent invokes the necessary tools (like get_forecasts) to pull data, formats the results, and gives you a clear answer in conversation.
Who is this actually for?
Data analysts who waste hours manually cross-referencing historical and current weather reports. Logistics managers running fleets across varied climates, needing immediate warning data for route changes. Anyone whose job depends on knowing the ground truth of a location's environment.
Uses get_alerts and get_conditions to confirm if adverse weather, like high winds or flood warnings, has grounded shipments along a planned route.
Combines historical data with real-time context by running get_observations and querying long-term patterns using get_conditions for research reports.
Checks local conditions and forecasts to schedule maintenance crews. They use get_places first to verify the coordinates, then check get_forecasts before dispatching teams.
What Changes When You Connect
Stop guessing the local time. Use get_places to pull exact timezone and coordinate information before you even ask about the weather, ensuring your query is anchored correctly.
You don't need multiple requests. Use get_batch to check alerts, current conditions, and a 7-day forecast all in one shot. It cuts out redundant API calls.
Need proof of what happened last month? The get_conditions tool handles historical data, giving you context beyond simple forecasts for deep analysis.
When coordinating field teams, rely on get_alerts. This tool monitors official sources like NWS and MeteoAlarm so you know about watches or advisories before they become problems.
Don't just assume the weather is fine. Use get_observations to get raw, measured METAR/PWS data—it's the ground truth for current conditions.
See it in action
Checking a supply route before shipment
A logistics manager needs to know if a cross-country truck route is safe. They ask their agent, which uses get_alerts and get_forecasts. The agent reports that while the current conditions are fine, there's a 'Flash Flood Watch' coming over the next 48 hours, forcing an immediate reroute.
Comparing historical climate data
A data analyst needs to write a report comparing seasonal average rainfall across three cities. They use get_conditions to pull historical precipitation records for the last five years and combine that with current coordinates gathered via get_places.
Verifying airport readiness
An air traffic controller needs immediate operational status at an airfield. They use get_observations to pull live METAR data for the specific airport ID, confirming visibility and wind speed instantly.
Planning a large outdoor event
The event planner asks their agent what the weather will be like over the next two weeks. The agent uses get_forecasts and warns that while today is clear, the chance of high winds increases dramatically mid-week.
The honest tradeoffs
Asking for data piecemeal
The user asks: 'What are the current conditions?' Then follows up with: 'And what's the forecast?'. This forces the agent to run multiple, separate API calls.
Combine your requests using get_batch. By listing all necessary endpoints in one go, you get a single response that contains both real-time observations and future forecasts.
Ignoring geographical context
A user asks for the 'weather in the city center' without specifying which time zone or coordinates are needed. The agent might return irrelevant data.
Always start by using get_places. This tool gives you the necessary foundational data, like accurate coordinates and timezone information, so subsequent weather calls work properly.
Overlooking official warnings
A user only checks general forecasts for a region but ignores active severe weather alerts. They might assume clear skies when dangerous conditions are imminent.
Before trusting any forecast, run get_alerts. This ensures you check authoritative sources (NWS, MeteoAlarm) for watches and advisories that override the standard prediction.
When It Fits, When It Doesn't
Use this MCP if your task requires specific, measurable environmental context—like knowing current wind speed or precipitation rates. You need it when planning logistics routes, writing climate reports, or coordinating field crews.
Don't use it if you just need a general idea of the weather ('Is it going to be nice?'). For simple, high-level questions, your agent might handle it fine. But for professional operations, you must check get_alerts and combine get_observations with get_forecasts. If all you need is a single location's time zone, just use get_places—don’t overcomplicate the call.
Questions you might have
How do I check the current weather observations using get_observations? +
Use get_observations and provide a city or airport ID. This retrieves the most recent, measured METAR/PWS data available for that specific location.
What is the best way to query multiple weather types at once? Use get_batch? +
Yep, get_batch is your tool. Instead of calling get_forecasts, then get_alerts, you list both in a single request for maximum efficiency.
Does AerisWeather help me with historical data? +
Yes, the get_conditions tool supports pulling historical weather datasets, letting you run reports based on past conditions and precipitation records.
I need to know if there are active warnings. Which tool should I use? get_alerts? +
get_alerts is the one. It checks official sources for current watches, advisories, and any severe weather warnings that might affect your area.
How do I get geographical data or time zone info using `get_places`? +
The tool provides detailed place information for cities, stations, and airports. You can pull essential context like coordinates and time zones, which is crucial before running specific weather queries.
Does `get_conditions` support minute-by-minute precipitation forecasts? +
Yes, you can get interpolated data for minutely precipitation forecasts. The tool supports this via a filter parameter and covers ranges up to 15 days out.
What kind of information do I get when I use `get_forecasts`? +
You receive general weather predictions for specified locations. This output is useful for planning future events, unlike the real-time data provided by observations or alerts.
Is there a standard format required for location inputs across all tools? +
No, the input format varies depending on what you need. For instance, get_observations accepts IDs like 'city,state', while get_places can handle names or station codes.
Can I get minute-by-minute precipitation forecasts? +
Yes! Use the get_conditions tool with the filter parameter set to minutelyprecip. This provides high-resolution precipitation data for the immediate future.
How do I check for active weather warnings in a specific area? +
Use the get_alerts tool and provide the location in the p parameter. It will return active watches, warnings, and advisories from official meteorological agencies.
Is it possible to search for cities or airports by name? +
Yes! Use the get_places tool with the action set to search and your query. It returns geographical metadata, timezones, and coordinates.
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