How to Use the USDA NASS MCP in Mastra AI

Q: What do I use for crop summaries with USDA NASS and Mastra AI?

Use getcropsummary. You feed it a commodity name, like SOYBEANS. It spits out detailed stats covering units and time periods that your workflow can then process.

Q: Can I find livestock data using USDA NASS and Mastra AI?

Yes. getlivestocksummary requires a commodity name—for example, CATTLE or HOGS—and returns the production summary for that animal type across states.

Q: How does the USDA NASS MCP Server help with parameter discovery in Mastra AI?

You call getparamvalues. This tool discovers what values are valid across all filter parameters (group, sector, state, etc.), giving your workflow agent the full context it needs before running any query.

Build complex USDA NASS workflows with Mastra AI.

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Connect USDA NASS MCP to Mastra AI

Create your Vinkius account to connect USDA NASS to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup USDA NASS with Mastra AI

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Perplexity

Handle Complex Economics for Mastra AI

When you need multi-step analysis, use `get_economics_data`. You can build a workflow that first checks the commodity, then filters by state, and finally pulls the historical economic data. Mastra AI handles retries. If one API call fails for some reason, your whole complex operation doesn't just die; it tries again automatically.

Get Demographics Data in Mastra AI

`get_demographics_data` provides farm demographics when you need them. The tool can optionally filter by state or year, letting your workflow focus on specific regions over time. Since this is a core component of the MCP Server, it fits right into complex decision trees, like 'If population drops below X, then check agricultural output.'

Query USDA NASS Quick Stats with Mastra AI

`get_quick_stats` lets you query broad statistical data. You set the sector (e.g., ANIMALS & PRODUCTS), and then narrow down to a commodity or state. This is perfect for building autonomous workflows that need to check dozens of related statistics across various time periods without manual intervention.

Setup guide

Set up USDA NASS MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All USDA NASS tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "usda-nass-mcp-client",
  servers: {
    "usda-nass-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "USDA NASS Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to USDA NASS tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent USDA NASS transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by USDA NASS. 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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Common questions about USDA NASS MCP in Mastra AI

Use `get_crop_summary`. You feed it a commodity name, like SOYBEANS. It spits out detailed stats covering units and time periods that your workflow can then process.

Yes. `get_livestock_summary` requires a commodity name—for example, CATTLE or HOGS—and returns the production summary for that animal type across states.

You call `get_param_values`. This tool discovers what values are valid across all filter parameters (group, sector, state, etc.), giving your workflow agent the full context it needs before running any query.

The server offers structured statistical data covering crops, livestock, demographics, and economics. Every query returns defined values along with specific units, states, and years.

It manages aggregated statistics concerning agricultural commodity yields. The data type handled is structured statistical measurements (e.g., CORN production units) across defined regions.

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