# NCDC Climate Data Online MCP > NCDC Climate Data Online connects your AI agent directly to the National Climatic Data Center's massive archive of historical weather records. You can pull precise temperature and precipitation data, find specific global weather stations, or map out entire regions using natural conversation. Stop navigating complex government APIs; just ask for the climate history you need. ## Overview - **Category:** the-unthinkable - **Price:** Free - **Tags:** climate-data, historical-weather, meteorology, environmental-monitoring, data-archiving ## Description Need to analyze how a certain location handled extreme heat back in 1985? This MCP lets your agent do that by talking directly to decades of authoritative NOAA data. Instead of manually searching through multiple databases and figuring out which station ID means what, you just tell the system what you need—a specific dataset for a date range or all stations within a county. The connector handles the complex querying, letting you focus on the science. When you connect this MCP via Vinkius, your agent gets instant access to over 4,000 other tools and can start building real-world climate models immediately. ## Tools ### get_climate_data Pulls actual climate data records based on specified location, date range, and metric. ### get_dataset Retrieves detailed information about a specific climate dataset by its identifier. ### get_station Fetches comprehensive metadata, coverage area, and details for one particular weather station. ### list_data_categories Lists the high-level scientific categories of data available in the system (e.g., Temperature, Precipitation). ### list_data_classes Shows the frequency classes for available data, such as Hourly, Daily, or Monthly summaries. ### list_data_types Lists specific measurable variables within a category, like Max Temperature or Snowfall. ### list_datasets Provides a list of major NCDC climate datasets available for querying (e.g., GHCND). ### list_location_categories Shows the hierarchy of geographical locations, like Country or State. ### list_locations Lists specific physical locations that can be used to filter data queries. ### list_stations Provides a list of all weather monitoring stations and their unique IDs across the globe. ## Prompt Examples **Prompt:** ``` List all weather stations in the city of Asheville, NC. ``` **Response:** ``` I've retrieved the weather stations for Asheville, NC. There are several active stations, including 'ASHEVILLE REGIONAL AIRPORT, NC US' (GHCND:USW00003812) and 'ASHEVILLE 13 S, NC US'. Would you like the detailed metadata for the airport station? ``` **Prompt:** ``` Get daily temperature data for station GHCND:USW00003812 for January 2023. ``` **Response:** ``` Fetching climate records... For January 2023 at Asheville Regional Airport, the average maximum temperature was 52°F (11°C) and the minimum was 34°F (1°C). There were 15 days with recorded precipitation. Shall I provide the full daily breakdown? ``` **Prompt:** ``` What climate datasets are available for global daily summaries? ``` **Response:** ``` Retrieving datasets... For daily summaries, the primary datasets are GHCND (Global Historical Climatology Network Daily) and GSOD (Global Summary of the Day). GHCND is generally recommended for its extensive station coverage. Would you like more info on GHCND? ``` ## Capabilities ### Retrieve historical weather records Get actual daily or monthly temperature and precipitation measurements for a specific location and date range. ### Find and inspect data sets List available climate datasets, like GHCND or GSOD, to understand what historical records exist globally. ### Locate weather stations by geography Search for specific physical monitoring sites worldwide, retrieving their full metadata and coverage details. ### Filter data by location type Browse structured location categories (like State or Country) to narrow down your geographical search boundaries. ### Determine available measurement types List specific scientific variables, such as Max Temperature or Snowfall, that the system tracks for a region. ## Use Cases ### Evaluating historical drought risk in the Midwest. An agronomist asks their agent: 'What was the average precipitation for Iowa between 2010 and 2015?' The agent uses `get_climate_data` to retrieve the records, allowing the agronomist to calculate regional yield volatility. ### Determining climate variability for insurance claims. An analyst needs to compare Hurricane Season 2018 data against 1958. They use `list_datasets` to confirm the right dataset and then execute multiple calls to `get_climate_data`, generating a comparative report. ### Writing a thesis on urban heat island effect. A student asks: 'Find all weather stations in Chicago's downtown area.' The agent uses `list_stations` and then the location filters to scope the search, providing metadata needed for the academic paper. ### Validating data coverage for a new monitoring project. A researcher asks: 'What are all available data classes in this region?' The agent uses `list_data_classes` and then checks specific variables using `list_data_types` to ensure the system tracks required metrics. ## Benefits - Instantly model trends: Use `get_climate_data` to pull precise, historical records for temperature and precipitation without writing complex SQL or API calls. - Understand the data source: If you don't know which dataset to use, run `list_datasets` first. It gives you a clear map of GHCND vs. GSOD. - Target specific points: Need info on one station? Use `get_station` to pull all metadata, ensuring your query uses the right identifier. - Drill down variables: Running through `list_data_categories`, followed by `list_data_types` lets you narrow a general query (like 'weather') down to exactly what you need (like 'Snowfall'). - Filter locations easily: Use `list_location_categories` and `list_locations` together. This prevents vague queries and locks your data to the correct city, state, or country. - Start simple: You don't have to learn 10 tools at once. Start by asking your agent for a list of stations (`list_stations`) and see where it takes you. ## How It Works The bottom line is that you get direct access to decades of official climate records without writing complex API calls or dealing with multi-step data pipelines. 1. First, subscribe to this MCP and provide your NCDC (NOAA) API Token. You'll need to grab this token from ncdc.noaa.gov. 2. Next, connect the MCP via your preferred client like Cursor or Claude. Your agent now has direct access to all historical data tools. 3. Finally, ask your AI client a natural language question—for instance, 'What was the average temperature in Asheville for January 2023?'—and it executes the necessary queries. ## Frequently Asked Questions **How do I find out what kind of weather data NCDC Climate Data Online can handle?** You use the `list_data_types` tool. This function gives you a list of all specific variables tracked by the system, such as Max Temperature or Snowfall, so you know exactly what to ask for. **What is the difference between GHCND and GSOD in NCDC Climate Data Online?** You can find out using `list_datasets`. These datasets are distinct archives; GHCND generally provides more detailed station coverage, while GSOD offers a different global summary format. **Can I get climate data for multiple stations at once with NCDC Climate Data Online?** Yes. First, use `list_stations` to find the IDs of all needed sites. Then, pass those IDs and your date range into a single query using `get_climate_data`. **What is required to use NCDC Climate Data Online MCP?** You must subscribe to the MCP and provide your personal NCDC (NOAA) API Token. This token grants your agent authenticated access to the official records. **How do I check what location types are available for data filtering?** Run the `list_location_categories` tool. This will show you if the system supports filtering by Country, State, County, or City, guiding your subsequent queries.