# NCDC Climate Data Online MCP > NCDC Climate Data Online lets you pull massive amounts of authoritative historical weather and climate data directly from NOAA's National Climatic Data Center. You can ask your agent for records—like daily temperature or precipitation totals—for specific locations across defined date ranges, no matter how complex the query gets. This MCP gives you access to discovery tools that let you list available datasets, find exact stations globally, and categorize data types like snowfall versus max temperature, all in one place. ## Overview - **Category:** the-unthinkable - **Price:** Free - **Tags:** climate-data, historical-weather, meteorology, environmental-monitoring, data-archiving ## Description You can use this connection to pull deep historical climate records using natural language conversation. Instead of navigating complex government websites, your agent talks directly to the National Climatic Data Center API, giving you access to a vast archive of weather history. Need to know how much rain fell in Asheville back in 2018? You ask for it. Want to compare average maximum temperatures across three different states over twenty years? Your agent handles the data gathering and retrieval. The MCP lets you look up specific stations globally, trace datasets like Global Historical Climatology Network Daily (GHCND), or filter by location type—city, country, county—to narrow down your scope. If you're finding yourself jumping between multiple APIs just to get a full picture of environmental trends, this MCP simplifies that. Vinkius organizes these complex data sources so you connect once and gain access to the entire NCDC catalog from any compatible client. ## Tools ### get_climate_data Pulls actual historical climate measurements like temperature or precipitation totals. ### get_dataset Fetches detailed information about a specific climate dataset available from NCDC. ### get_station Retrieves complete metadata and coverage details for a single weather monitoring station. ### list_data_categories Lists all major types of data available, such as temperature and precipitation. ### list_data_classes Shows the time granularity options for data, like hourly or monthly summaries. ### list_data_types Lists specific metrics you can track, such as Max Temperature or Snowfall depth. ### list_datasets Retrieves a list of all available NCDC climate datasets (e.g., GHCND, GSOD). ### list_location_categories Shows the types of geographic boundaries you can query, like City or Country. ### list_locations Provides a list and ID for specific physical locations (e.g., New York City). ### list_stations Lists all active weather stations within a defined geographical area. ## 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 specific historical records Pull actual climate measurements—like temperature or rainfall totals—for a defined location and date range. ### Discover available datasets List and inspect the full catalog of climatic data sets, including global summaries and daily records. ### Identify weather stations globally Locate specific monitoring stations worldwide and retrieve their full metadata details. ### Filter by location type Browse pre-defined categories—like Country, State, or City—to scope down your data queries efficiently. ### Define data metrics and timeframes List what kinds of data are available (e.g., Max Temperature, Snowfall) and how granular you need the results to be (hourly, monthly). ## Use Cases ### Modeling extreme weather events A climate scientist needs to model a flood risk from 20 years ago. They ask their agent to pull data for 'river gauge XYZ, State A' using list_stations and then use get_climate_data to retrieve precipitation records over the necessary time window. ### Assessing agricultural crop yield An agronomist needs to compare historical growing seasons. They first run list_location_categories to confirm 'County' is a valid filter, then use get_climate_data to pull temperature and precipitation records for several counties in sequence. ### Analyzing insurance loss potential An analyst must determine if a specific region was prone to deep freezes. They ask the agent to list_data_types for 'Minimum Temperature' and then use get_climate_data, specifying the location ID retrieved via list_locations. ### Completing an academic paper A student needs global summary data. They run list_datasets to find GHCND, then prompt for 'daily temperature averages in Southeast Asia' which uses get_climate_data after the agent handles all filtering. ## Benefits - Automate data discovery: Instead of reading manuals, you use list_datasets and list_data_categories to quickly scope the exact climate datasets (like GHCND) needed for your project. - Pinpoint stations anywhere: Use list_stations and get_station to find metadata for a specific monitoring point globally. You'll know exactly what data coverage area you're working with. - Structure complex queries: Need annual averages of Max Temperature? First, use list_data_types and then run get_climate_data, letting your agent handle the date range and metric selection automatically. - Understand location boundaries: Use list_location_categories and list_locations to define a query by city or county ID, eliminating guesswork when pulling regional data. - Handle temporal flexibility: The MCP lets you shift between hourly, daily, and monthly records using list_data_classes without rewriting your logic. You just change the time frame in your request. ## How It Works The bottom line is you talk to your AI client like you're talking to a human researcher, and it handles all the complex data connection steps behind the scenes. 1. Subscribe to this MCP on Vinkius and enter your NCDC API Token from NOAA. 2. Your agent sends a natural language request, specifying what data you want and where. 3. The MCP translates that request into the necessary calls to the NCDC API and returns the structured climate records. ## Frequently Asked Questions **How do I find out what kinds of data I can analyze with NCDC Climate Data Online MCP?** You start by running list_data_categories to see the main groups, and then use list_data_types to get specific metrics like 'Snowfall' or 'Max Temperature'. **Do I need a location ID before using NCDC Climate Data Online MCP?** Yes. Use list_location_categories first, and then use list_locations to retrieve the exact required ID for your query. **What is the best way to find nearby weather stations?** First, you run list_stations to get a general list, or if you know an area, you can use list_locations and then ask your agent to pull all associated station details using get_station. **Can I compare different climate datasets?** Yes. You first run list_datasets to identify the specific dataset names (like GHCND) you need, and then use get_climate_data to pull comparable records from each one. **How does NCDC Climate Data Online MCP handle date ranges?** The agent manages the temporal flexibility. You specify the start and end dates in your query, and it handles fetching data across various time classes (hourly, daily, monthly).