How to Use the ERS USDA (Economic Research) MCP in OpenAI Agents SDK

Q: How do I configure ERS USDA (Economic Research) with OpenAI Agents SDK?

Install openai-agents via pip. Create an MCPServerStreamableHttp instance with your Vinkius endpoint URL and pass it into the mcpservers array in your Agent constructor.

Q: Does OpenAI Agents SDK cache the ERS USDA (Economic Research) MCP tool definitions?

Set cacheToolsList=True during initialization. This prevents the agent from fetching the schema for all seven USDA tools on every single turn, saving tokens and reducing latency.

Q: What happens if the agent misses a required parameter?

The framework catches it locally. Since getarmssurveydata requires a year and either a report or variable, the SDK blocks the execution and returns an error to the agent to fix the missing argument.

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Connect ERS USDA (Economic Research) MCP to OpenAI Agents SDK

Create your Vinkius account to connect ERS USDA (Economic Research) to OpenAI Agents SDK 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 ERS USDA (Economic Research) with OpenAI Agents SDK

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Pull Survey Data Automatically

The `get_arms_surveydata` tool pulls historical farm production metrics into your agent's context. You pass a specific year and variable to retrieve exact financial records from the Agricultural Resource Management Survey. OpenAI Agents SDK validates every parameter before hitting the USDA endpoint. If your agent tries to query without the mandatory year argument, the built-in guardrails block the request and prompt the model to correct its inputs.

Map Farm Types with the MCP Server

The `get_arms_farmtypes` tool exposes structural classifications for different agricultural operations. Your agent reads this metadata to understand how the USDA categorizes family-owned versus corporate farms. You can build a handoff system where a primary agent pulls the structural data, then passes it to a specialized financial agent. That second agent then runs `get_arms_reports` to match those farm types against available debt-to-asset ratio reports.

Cross-Reference State Metadata

The `get_arms_states` tool returns regional identifiers required for localized economic analysis. Your agent calls this to map state codes before pulling regional survey results. Tracing through the OpenAI dashboard shows exactly when the agent calls `get_arms_variables` to verify variable definitions. You see the raw inputs and the exact USDA ERS definitions returned, making it easy to audit the economic reasoning.

Setup guide

Set up ERS USDA (Economic Research) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all ERS USDA (Economic Research) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives ERS USDA (Economic Research) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate ERS USDA (Economic Research) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="ERS USDA (Economic Research) Agent",
            instructions="You have access to ERS USDA (Economic Research) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 ERS USDA. 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 ERS USDA (Economic Research) MCP in OpenAI Agents SDK

Install `openai-agents` via pip. Create an `MCPServerStreamableHttp` instance with your Vinkius endpoint URL and pass it into the `mcp_servers` array in your Agent constructor.

Yes, but it requires sequential calls. The agent first uses `get_arms_years` to find available timeframes, then loops through `get_arms_surveydata` for each specific year to build a historical trend.

Set `cacheToolsList=True` during initialization. This prevents the agent from fetching the schema for all seven USDA tools on every single turn, saving tokens and reducing latency.

The framework catches it locally. Since `get_arms_surveydata` requires a year and either a report or variable, the SDK blocks the execution and returns an error to the agent to fix the missing argument.

Your agent accesses aggregated USDA agricultural survey results and state-level metadata. Vinkius runs the MCP connection inside a V8 Isolate Sandbox. The session is ephemeral, meaning no financial variables or query histories persist after the agent disconnects.

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