How to Use the Agro MCP in OpenAI Agents SDK

Build production-grade agricultural agents with OpenAI Agents SDK that monitor fields, weather, and soil data reliably.

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Connect Agro MCP to OpenAI Agents SDK

Create your Vinkius account to connect Agro 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 Agro with OpenAI Agents SDK

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Define and Track Field Boundaries

Your agent can now manage agricultural land parcels directly. Use it to map new fields with `create_polygon`, adjust boundaries with `update_polygon`, or get a full list of monitored areas with `list_polygons`. It’s a simple way to give your agent spatial awareness. This is where the SDK's production focus really shines. Its built-in guardrails help prevent your agent from accidentally wiping out the wrong field with `delete_polygon`. You also get full execution tracing in the OpenAI dashboard, so you can review exactly which actions your agent took and why.

Get Real-Time Environmental Data

This isn't just a chatbot; it's an active monitoring system. Your agent can pull up-to-the-minute weather, soil conditions, and UV index for any polygon you've defined. It just needs to call tools like `get_current_weather`, `get_current_soil`, and `get_current_uvi` to get a live snapshot. The OpenAI SDK architecture allows for sophisticated workflows. For example, you could have one specialized agent that only monitors forecasts using `get_forecast_weather`. If it detects an issue, it can hand the task off to another agent to check soil moisture and alert a user, all within a managed, traceable environment.

Analyze Historical Trends with this MCP Server

Go beyond what's happening right now. You can instruct your agent to analyze past performance by pulling historical data for any field. It can request past weather patterns, soil data, or satellite vegetation indexes using `get_historical_weather`, `get_historical_soil`, and `get_ndvi_history`. The async nature of the SDK means your agent can fetch multiple datasets concurrently without blocking your application. Setting `cacheToolsList=True` in your setup ensures the agent discovers all available Agro tools from this MCP Server instantly on startup, which makes your production system faster.

Setup guide

Set up Agro 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 Agro tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Agro 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="Agro Agent",
            instructions="You have access to Agro tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Agro MCP in OpenAI Agents SDK

Your agent automatically discovers all 17 Agro tools on connection. You can then instruct it to, for example, create a polygon for a field and then get its historical NDVI data. The SDK handles the API calls and provides tracing.

Not directly through the MCP integration, which is designed for full tool discovery. You can, however, build guardrails into your agent's own logic to restrict it from calling specific tools, like `delete_polygon`.

It's pretty quick. After installing the SDK, you instantiate `MCPServerStreamableHttp` with your Vinkius endpoint URL. Then you just pass that server object into the `mcp_servers` list in your Agent's constructor.

Yes. The server responds quickly to requests for current data like `get_current_weather`. Paired with the async structure of the OpenAI SDK, you can build agents that poll for changes and trigger alerts without noticeable delay.

The server only handles the agricultural and location data you explicitly send it, like polygon coordinates. It never touches personal user info. Vinkius secures the connection with a unique endpoint token, and every operation runs in a zero-trust, ephemeral sandbox to keep your field data isolated.

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