NOAA Forecast MCP. Get official US weather data in minutes.
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NOAA Forecast — US Weather Predictions provides direct access to official National Weather Service data. Get 7-day daily forecasts (highs/lows, precip), detailed hourly conditions (156 hours), raw quantitative grid arrays (temperature, wind, humidity), and technical forecast discussions from NWS meteorologists across 122 offices.
It covers the entire US mainland, Puerto Rico, Guam, and US territories.
What your AI agents can do
Get forecast
Gets a 7-day weather forecast (high/low temps, wind, precip) using the latitude and longitude of any US location.
Get forecast discussion
Retrieves the Area Forecast Discussion (AFD) from an NWS Weather Forecast Office using its 3-letter code.
Get grid data
Pulls raw, quantitative weather data arrays—including temperature, precipitation, wind, and humidity—for programmatic analysis across the US.
Pass latitude and longitude for any US location to get a daily summary of high/low temps, wind speed/direction, and precipitation probability.
Fetch hour-by-hour weather conditions over 5 days, including temperature, wind, humidity, precipitation likelihood, and sky condition for a US location.
Pull raw quantitative data arrays—temperature, precipitation, wind, and humidity—useful for complex, programmatic analysis.
Retrieve the Area Forecast Discussion (AFD) from a specific NWS Weather Forecast Office using its 3-letter WFO code.
Pull technical details for a US location, including which WFO is responsible and its grid coordinates/zones.
Ask AI about this MCP
Supported MCP Clients
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NOAA Forecast — US Weather Predictions: 5 Tools
These five tools give your agent every data stream needed to model weather conditions—from simple 7-day outlooks to raw, quantitative grid arrays.
019d75deget forecast
Gets a 7-day weather forecast (high/low temps, wind, precip) using the latitude and longitude of any US location.
019d75deget forecast discussion
Retrieves the Area Forecast Discussion (AFD) from an NWS Weather Forecast Office using its 3-letter code.
019d75deget grid data
Pulls raw, quantitative weather data arrays—including temperature, precipitation, wind, and humidity—for programmatic analysis across the US.
019d75deget hourly forecast
Fetches hour-by-hour weather conditions for a US location over 5 days, covering temperature, wind, and chance of rain.
019d75deget point metadata
Gathers NWS metadata for a US location, providing the responsible WFO, grid coordinates, and zone information.
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What you can do with this MCP connector
You're hooking your agent up directly to the National Weather Service data feed—the real deal for US predictions. This isn't some basic weather app; you're getting access to official NWS data covering the entire mainland, Puerto Rico, Guam, and all US territories.
When you need a quick look at what's coming up, use get_forecast. Just feed it the latitude and longitude for any location, and you'll get a full seven-day daily summary. That gives you high/low temps, wind speed and direction, and the probability of precipitation for every day.
Need more granularity? You can run get_hourly_forecast to pull hour-by-hour conditions over five days. This covers temperature, wind, humidity levels, chance of rain, and sky condition for that specific spot. It's way deeper than a simple daily outlook.
For the real deep dive—the stuff you use in complex modeling—you can run get_grid_data. This pulls raw, quantitative weather arrays across the whole US. You get temperature, precipitation, wind, and humidity data ready for programmatic analysis. It's meant for crunching numbers, not just reading them.
If you need to know where that location falls in NWS terms, use get_point_metadata. This function pulls the technical details: which Weather Forecast Office (WFO) is responsible, and its specific grid coordinates and zone info. It tells you exactly who's running point for that area.
To understand the expert context behind the numbers, run get_forecast_discussion. You just need the 3-letter WFO code, and it pulls the Area Forecast Discussion (AFD). These are technical reports written by actual NWS meteorologists—they give you the 'why' behind the forecast.
Your AI client can manage all this. It lets you pull daily summaries with get_forecast, five days of hourly data with get_hourly_forecast, raw gridded arrays using get_grid_data, technical WFO context via get_point_metadata, and expert narrative discussion through get_forecast_discussion.
How NOAA Forecast MCP Works
- 1 First, provide your AI client with the specific geographic input: either latitude/longitude for forecasts or a 3-letter WFO code for discussions.
- 2 Your agent then selects the right tool—e.g.,
get_hourly_forecastif you need 156 hours of data, orget_grid_dataif you need raw arrays. - 3 The system executes the call and returns structured JSON containing all requested weather metrics for US locations only.
The bottom line is: it gives your agent a single, reliable connection to deep NWS data without needing an API key or complex setup.
Who Is NOAA Forecast MCP For?
Disaster response teams who need rapid weather modeling. Energy grid engineers tracking load requirements. Data scientists building predictive climate models. Anyone whose job requires reliable, official US weather data inputs.
Uses get_forecast_discussion to pull the latest technical Area Forecast Discussion (AFD) from NWS offices for detailed analysis.
Runs get_grid_data and get_hourly_forecast to model anticipated load changes based on temperature, wind, and precipitation across a region.
Uses latitude/longitude inputs with get_forecast or get_point_metadata to build datasets for predictive modeling of weather patterns.
What Changes When You Connect
- Access raw metrics with
get_grid_data. You get quantitative arrays for temperature, wind, and precipitation—not just averages. This is essential when you need to feed structured numbers into a modeling script. - Move beyond simple daily summaries using
get_hourly_forecast. It provides 156 hours of detail, letting your agent build time-series models that predict conditions hour by hour for the next five days. - Understand local context with
get_point_metadata. You can determine which specific Weather Forecast Office (WFO) is responsible for a given point, ensuring your data source is correctly attributed and localized. - Stay ahead of regional shifts using
get_forecast_discussion. Instead of relying on general reports, you pull the technical Area Forecast Discussion (AFD) directly from NWS meteorologists' notes. - Keep it simple with
get_forecast. Need a quick 7-day snapshot for planning? This tool handles daily highs, lows, and precipitation chances using just latitude and longitude.
Real-World Use Cases
Planning a multi-week outdoor event.
The Event Planner needs to know if the ground will be wet or dry over the next month. Instead of checking multiple websites, they ask their agent for the get_hourly_forecast over 156 hours and analyze precipitation probability across all available days.
Modeling regional energy demand.
The Energy Engineer needs to predict power spikes. They use get_grid_data to pull raw temperature, wind speed, and humidity arrays into a script, letting the data determine load requirements rather than general forecasts.
Analyzing extreme weather patterns.
The Disaster Manager needs technical context. They ask for get_forecast_discussion using the WFO code to read the raw NWS meteorologist notes, understanding why a storm is predicted, not just that it will happen.
Building an asset tracking system.
The Logistics Manager needs to know if equipment can travel through a specific county. They run get_point_metadata first to confirm the zone and then use get_forecast with coordinates to check 7-day conditions.
The Tradeoffs
Using it for non-US locations
Trying to get weather data for Paris, France. The agent runs a generic request and fails because the NOAA API only supports US territories.
→ This server is strictly limited to the United States and its defined territories (Puerto Rico, Guam). If you need international weather, use a different category of forecasting service.
Confusing raw data with summaries
Assuming that get_forecast's average temperature is enough for a power model. This misses the variability needed for accurate load calculation.
→
For engineering analysis, use get_grid_data. It gives you the raw quantitative arrays (temp, wind, humidity) so your script can handle variations and ranges.
Over-relying on simple search queries
Asking 'What's the weather in Seattle?' The agent might get a vague response that doesn't give enough detail for an actionable report.
→
Specify your need. If you want hourly data, use get_hourly_forecast and provide coordinates. This forces the right level of detail.
When It Fits, When It Doesn't
Use this MCP Server if: 1) You require official National Weather Service (NWS) data for US locations; 2) Your application needs raw, technical metrics like wind arrays or hourly precipitation probability; or 3) You need the expert context provided by Area Forecast Discussions. Use get_grid_data when your workflow requires numerical inputs for calculation. Use get_forecast_discussion when you need to understand why NWS thinks it will rain.
Don't use this if: 1) You only want a simple, general city summary (a basic search engine is faster). 2) Your location is outside the US/PR/Guam territories. 3) You are trying to predict weather far into the future—the data is based on established forecast models.
It's best for developers who know exactly what metric they need and how to process it.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NOAA. 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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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 server provides 5 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Checking the weather used to mean clicking through multiple, unformatted websites.
Today, if you needed a full picture—say, 7 days of data plus hourly details for three different locations—you’d open NOAA.gov, hit the daily forecast tab, copy the high/lows, then repeat that process on the hourly page for location B, and finally maybe search for the WFO discussion separately. It's slow, requires manual cross-referencing, and you lose context every time.
With this MCP server, your agent handles it all in one query chain. You tell it: 'I need a 7-day forecast plus raw grid data for coordinates X, Y.' The result comes back structured, clean, and ready to drop straight into your database or script.
Using NOAA Forecast — US Weather Predictions MCP Server gives you the technical depth needed.
The biggest manual pain point is getting the *why*. General forecasts tell you 'rain expected.' But if you're running a sophisticated model, you need to know the source: Was it low-pressure system X? What does the NWS meteorologist think about it? You used to have to hunt down and read the raw Area Forecast Discussion (AFD) document.
Now, your agent calls `get_forecast_discussion` using just the WFO code. It pulls the full technical text directly into your workflow. This isn't a summary; it’s the expert analysis itself. That changes everything.
Common Questions About NOAA Forecast MCP
How do I get hourly data using `get_hourly_forecast`? +
get_hourly_forecast requires latitude and longitude for a US location. It returns conditions over 5 days, covering temperature, wind, humidity, and precipitation likelihood hour by hour.
Is the raw data from `get_grid_data` usable in Python? +
Yes, get_grid_data returns quantitative arrays for temp, wind, precip, and humidity. This structured format makes it ideal for direct programmatic analysis or feeding into numerical models.
`get_forecast_discussion` needs a WFO code—what is that? +
The WFO (Weather Forecast Office) is the specific NWS regional office responsible for an area. You need its 3-letter code, like 'OKX' or 'LAX', to retrieve the technical Area Forecast Discussion.
What coordinates should I use with `get_forecast`? +
get_forecast needs a precise latitude and longitude pair for any location within the United States. Using these coordinates ensures accurate, localized 7-day forecasting results.
If I try to use `get_forecast` for a location outside the US, will it work? +
No, this tool only works within U.S. borders. The NOAA forecast engine explicitly covers the United States and its territories. You won't get data for locations in Canada or Mexico using any of the provided tools.
How do I retrieve the full text when using `get_forecast_discussion`? +
You must use the initial call to fetch the product ID first. The API provides a list of IDs, and you need to pass that specific product ID back into the function to get the complete Area Forecast Discussion (AFD) text.
When I run `get_point_metadata`, are there any data quality checks for my coordinates? +
The tool expects standard latitude and longitude pairs. While it doesn't validate geographic accuracy, using precise decimal degree coordinates ensures you get the correct Responsible WFO assignment, grid zone info, and metadata.
What should I do if I need to run `get_hourly_forecast` multiple times in a short period? +
Be mindful of rate limits. For high-volume retrieval, implement a backoff strategy or check the service's published usage guidelines. Rapid calls may result in temporary API throttling.
Does this work outside the US? +
No. The NWS API only covers the United States, Puerto Rico, Guam, US Virgin Islands, and American Samoa. For global weather, consider the Open-Meteo MCP server.
Use it with your favorite AI tools
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