USDA NASS MCP. Analyze crop yields and farm economics instantly.

Connecting your AI client to the National Agricultural Statistics Service (NASS) API means you can query deep, detailed US data on farming—from corn yields to cattle counts—using plain English. You don't have to wrestle with those complicated government websites; your agent handles all that dirty work.

Crop Production Summaries: The get_crop_summary tool lets you pull production totals and yield stats for major crops like soybeans or wheat. You can narrow down the results by specific states or years, so you get exactly what you need.

Livestock Inventory Totals: If you're tracking animal populations, get_livestock_summary provides current inventory counts and production summaries for commodities like cattle, hogs, chickens, milk, or eggs. It gives you the latest numbers across those sectors.

Agricultural Economic Data: You can get financial metrics by running get_economics_data. This pulls stuff like prices paid to farmers, commodity expenses, or even land values for a specific state and year. It's all tied together by commodity.

Farm Demographics: Need to know who’s working the farms? The get_demographics_data tool retrieves population data about farm operators. You can filter this down by age, occupation, or even pinpoint it to a specific state and year.

Search Broad Commodity Data Sets: When you're just trying to figure out what data exists for a certain commodity—say, 'barley'—you run search_by_commodity. This gives you a wide search across all available stats: production numbers, price history, and inventory counts. It’s your starting point.

General Data Querying: The get_quick_stats tool lets you run a general query against the whole NASS database. You feed it multiple filters—like sector, state, year, or if you want annual or monthly figures—and it runs the report. It’s super flexible.

Parameter Discovery: Before you hit send on any major query, use get_param_values. This tool lists all the valid values for every filter NASS uses—states, commodities, units. You'll know exactly what parameters to plug in, which saves a ton of time.

Data Metadata Lookup: If you want to know what data set exists or how often it gets updated, get_survey_info displays metadata about the USDA NASS surveys. It tells you precisely what data they collect and the frequency of that collection. You'll also use get_demographics_data if you wanna see population trends by specific sectors.

Commodity Identification: To make sure you’re using the right terms, you can run a broad search for all available statistics—production, price, or inventory—for one specific commodity name. This is how you nail down your subject matter before building the final report.

When you use these tools together, your AI client acts like an expert agricultural analyst. You don't just get data; you get full context on farming economics and demographics that you can ask for in plain English.