
This server connects Claude to the USDA's National Agricultural Statistics Service Quick Stats database, letting you query U.S. crop production, livestock inventory, prices, and farm economics data. It's part of the Pipeworx gateway, which routes requests to 673+ data sources and includes an ask_pipeworx tool that translates plain English questions into the right API calls automatically. Useful when you need historical or current ag data for market analysis, research, or building tools around farm economics. The streamable HTTP transport means no local installation. You can connect to just the NASS endpoint or use the full Pipeworx gateway for access to everything at once.
The USDA National Agricultural Statistics Service. Crop production, livestock inventory, prices, planted/harvested acres, yields — the official US agricultural data. State and county level for major commodities. Free, no auth (light rate limit).
Part of Pipeworx — an MCP gateway connecting AI agents to 1476+ live data sources.
For agricultural commodity questions — supply outlook, price trends, regional production — NASS is the authoritative source. Used by USDA's own analysts, commodity traders, and agribusiness. Pair with BLS for ag wages/PPI and Comtrade for global trade flows.
Common flows:
nass_query({commodity: "CORN", agg_level: "NATIONAL"}) → annual and monthly forecasts.state: "IOWA" for state-specific.nass_query({commodity: "CATTLE", state: "TEXAS"}).Used by the agricultural_commodity_brief recipe.
NASS Quickstats requires a free API key from https://quickstats.nass.usda.gov/api. Pass via _apiKey. Without a key, calls fail; with one, generous limits.
Field crops: corn, soybeans, wheat, cotton, rice, sorghum, barley, oats, peanuts, sugar.
Livestock: cattle (calves, steers, dairy), hogs, sheep, poultry (broilers, layers, turkeys).
Specialty: vegetables (potatoes, tomatoes), fruit (apples, citrus), tree nuts, ornamentals.
Use exact USDA commodity names — the API is finicky about spelling and capitalization.
| Report | Frequency |
|---|---|
| Crop Production | Monthly during growing season; final in January |
| Quarterly Hogs and Pigs | March, June, September, December |
| Cattle Inventory | January, July |
| Prices Received / Paid | Monthly |
| Crop Progress (planted / harvested %) | Weekly during growing season |
The crop-progress reports are the highest-frequency signal.
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"nass": {
"url": "https://gateway.pipeworx.io/nass/mcp"
}
}
}
tools/list at https://gateway.pipeworx.io/nass/mcp returns the tools in the table
above plus the shared Pipeworx meta-tools — ask_pipeworx,
discover_tools, search_within, remember/recall and the rest of the
gateway-wide set. So the tool count you see is larger than this table: a
single-pack endpoint currently lists roughly 30 shared tools alongside the
pack's own. The connection's initialize response states its exact scope, and
is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a
scoped connection answer a question this pack does not cover — via
ask_pipeworx, which routes across the whole catalog — without you adding a
second MCP server. There is currently no way to mount a pack endpoint without
them; if the extra schemas cost you more context than the routing is worth,
connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}
Both URLs reach the same gateway and the same 1476+ data sources. The
only difference is which pack's tools are listed directly; ask_pipeworx
reaches all of them from either one.
Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:
ask_pipeworx({ question: "your question about Nass data" })
The gateway picks the right tool and fills the arguments automatically.
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