How to Use the Farmonaut MCP in OpenAI Agents SDK

Q: How do I connect Farmonaut with OpenAI Agents SDK?

You instantiate the MCPServerStreamableHttp class with your endpoint and pass it to the agent constructor. This links the tools directly, allowing your agent to call Farmonaut functions as native methods.

Q: How are field boundaries updated in OpenAI Agents SDK?

You pass your GeoJSON polygons through the addfield tool. The agent handles the registration and immediately returns the field ID for future queries.

Integrate Farmonaut satellite data into your OpenAI Agents SDK builds for direct field monitoring and automated crop health tracking.

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Connect Farmonaut MCP to OpenAI Agents SDK

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

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Automated crop health tracking via OpenAI Agents SDK

Your agent pulls real-time `get_ndvi` and `get_evi` data to check plant vigor across your specific plots. It skips the manual data entry, letting your agent flag stress points before they hit your yield. By feeding this data into your agent's reasoning loop, it correlates vegetation health with historical weather patterns. You get a clear picture of what's happening in your soil without leaving your development environment.

Precision irrigation management for your agents

Use `get_soil_moisture` and `get_ndwi` to trigger irrigation logic based on actual water stress levels. The agent compares these readings against `get_weather_forecast` to decide if the current moisture levels will hold or if the pumps need to kick in. This setup prevents over-watering by tying your hardware controls directly to satellite-derived moisture indices. It's a closed-loop system where your agent acts as the field manager.

Field onboarding for OpenAI Agents SDK systems

Use `add_field` to register new boundaries directly from your agent's interface. Once defined, the system pulls initial baselines using `get_fields` so your agent has the context it needs to run monitoring tasks. This makes scaling your operation simple. Your agent maintains the inventory, tracks the planting dates, and keeps the monitoring active for every new plot you bring under management.

Setup guide

Set up Farmonaut 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 Farmonaut tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Farmonaut 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="Farmonaut Agent",
            instructions="You have access to Farmonaut 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 Farmonaut MCP in OpenAI Agents SDK

You instantiate the `MCPServerStreamableHttp` class with your endpoint and pass it to the agent constructor. This links the tools directly, allowing your agent to call Farmonaut functions as native methods.

Yes. Since you are working in a production environment, you can wrap the tool outputs in your own guardrails. This ensures the satellite data is verified before your agent initiates any field actions.

It does. You can cache your tool list to keep performance high while the agent iterates over hundreds of fields. The SDK handles the async context, keeping your monitoring pipeline responsive.

You pass your GeoJSON polygons through the `add_field` tool. The agent handles the registration and immediately returns the field ID for future queries.

Your field boundaries and sensor readings are encrypted in transit. Only the specific coordinates you provide to the server are processed to generate your indices, and no raw data is stored outside your control.

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