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ERS USDA (Economic Research) MCP Server for Mastra AIGive Mastra AI instant access to 7 tools to Get Arms Categories, Get Arms Farmtypes, Get Arms Reports, and more

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Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect ERS USDA (Economic Research) through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.

Ask AI about this MCP Server for Mastra AI

The ERS USDA (Economic Research) MCP Server for Mastra AI is a standout in the Data Analytics category — giving your AI agent 7 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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

async function main() {
  // Your Vinkius token. get it at cloud.vinkius.com
  const mcpClient = await createMCPClient({
    servers: {
      "ers-usda-economic-research": {
        url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
      },
    },
  });

  const tools = await mcpClient.getTools();
  const agent = new Agent({
    name: "ERS USDA (Economic Research) Agent",
    instructions:
      "You help users interact with ERS USDA (Economic Research) " +
      "using 7 tools.",
    model: openai("gpt-4o"),
    tools,
  });

  const result = await agent.generate(
    "What can I do with ERS USDA (Economic Research)?"
  );
  console.log(result.text);
}

main();
ERS USDA (Economic Research)
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ERS USDA (Economic Research) MCP Server

Connect to the USDA Economic Research Service (ERS) and query the Agricultural Resource Management Survey (ARMS) directly. This server provides comprehensive access to the primary source of information on the financial condition, production practices, and resource use of America's farm businesses.

Mastra's agent abstraction provides a clean separation between LLM logic and ERS USDA (Economic Research) tool infrastructure. Connect 7 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.

What you can do

  • Survey Data Retrieval — Fetch detailed financial and production data from U.S. farms using specific years, reports, or variables.
  • Geographic Analysis — List all available ARMS States and retrieve metadata specific to regional agricultural economies.
  • Historical Trends — Access all available survey years to perform longitudinal analysis of farm income and expenses.
  • Variable Metadata — Inspect detailed definitions and metadata for variables used in the ARMS dataset to ensure accurate data interpretation.
  • Farm Classification — Query specific farm types and categories (like farm typology or operator households) to segment your research.

The ERS USDA (Economic Research) MCP Server exposes 7 tools through the Vinkius. Connect it to Mastra AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 7 ERS USDA (Economic Research) tools available for Mastra AI

When Mastra AI connects to ERS USDA (Economic Research) through Vinkius, your AI agent gets direct access to every tool listed below — spanning agriculture, economic-data, survey-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

get

Get arms categories on ERS USDA (Economic Research)

List ARMS categories and subcategories

get

Get arms farmtypes on ERS USDA (Economic Research)

Get all ARMS Farm Types

get

Get arms reports on ERS USDA (Economic Research)

Get available ARMS reports and variables

get

Get arms states on ERS USDA (Economic Research)

Get all ARMS States and available metadata

get

Get arms surveydata on ERS USDA (Economic Research)

S. farms. Requires year AND at least one of report or variable. Retrieve ARMS survey results

get

Get arms variables on ERS USDA (Economic Research)

Get detailed metadata for ARMS variables

get

Get arms years on ERS USDA (Economic Research)

Get all available ARMS years

Connect ERS USDA (Economic Research) to Mastra AI via MCP

Follow these steps to wire ERS USDA (Economic Research) into Mastra AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install dependencies

Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.ts and run with npx tsx agent.ts
04

Explore tools

Mastra discovers 7 tools from ERS USDA (Economic Research) via MCP

Why Use Mastra AI with the ERS USDA (Economic Research) MCP Server

Mastra AI provides unique advantages when paired with ERS USDA (Economic Research) through the Model Context Protocol.

01

Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add ERS USDA (Economic Research) without touching business code

02

Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation

03

TypeScript-native: full type inference for every ERS USDA (Economic Research) tool response with IDE autocomplete and compile-time checks

04

One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure

ERS USDA (Economic Research) + Mastra AI Use Cases

Practical scenarios where Mastra AI combined with the ERS USDA (Economic Research) MCP Server delivers measurable value.

01

Automated workflows: build multi-step agents that query ERS USDA (Economic Research), process results, and trigger downstream actions in a typed pipeline

02

SaaS integrations: embed ERS USDA (Economic Research) as a first-class tool in your product's AI features with Mastra's clean agent API

03

Background jobs: schedule Mastra agents to query ERS USDA (Economic Research) on a cron and store results in your database automatically

04

Multi-agent systems: create specialist agents that collaborate using ERS USDA (Economic Research) tools alongside other MCP servers

Example Prompts for ERS USDA (Economic Research) in Mastra AI

Ready-to-use prompts you can give your Mastra AI agent to start working with ERS USDA (Economic Research) immediately.

01

"Show me all available years for the ARMS survey data."

02

"Get the income statement survey data for the year 2022."

03

"List the different farm types available in the ERS ARMS dataset."

Troubleshooting ERS USDA (Economic Research) MCP Server with Mastra AI

Common issues when connecting ERS USDA (Economic Research) to Mastra AI through Vinkius, and how to resolve them.

01

createMCPClient not exported

Install: npm install @mastra/mcp

ERS USDA (Economic Research) + Mastra AI FAQ

Common questions about integrating ERS USDA (Economic Research) MCP Server with Mastra AI.

01

How does Mastra AI connect to MCP servers?

Create an MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.
02

Can Mastra agents use tools from multiple servers?

Yes. Pass multiple MCP clients to the agent constructor. Mastra merges all tool schemas and the agent can call any tool from any server.
03

Does Mastra support workflow orchestration?

Yes. Mastra has a built-in workflow engine that lets you chain MCP tool calls with branching logic, error handling, and parallel execution.

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