How to Use the EOSDA Agriculture Satellite Data MCP in OpenAI Agents SDK

Build production-ready crop monitoring pipelines using the OpenAI Agents SDK and live satellite telemetry.

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Connect EOSDA Agriculture Satellite Data MCP to OpenAI Agents SDK

Create your Vinkius account to connect EOSDA Agriculture Satellite Data 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 EOSDA Agriculture Satellite Data with OpenAI Agents SDK

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OpenAI Agents SDK Auto-Discovery

Your agent needs to know what sensors are flying over a target field. Drop this MCP Server into your Python environment, and it automatically exposes `get_available_datasets` to your OpenAI setup. No manual routing required. You define the guardrails while the agent handles the heavy lifting. It can pull lists of supported metrics using `get_available_indices` before it ever commits to running a heavy analysis job.

Trigger Multi-Sensor Vegetation Tasks

Stop writing custom polling logic for asynchronous satellite processing. Your production agent calls this MCP Server to request NDVI or EVI calculations for a specific geometry via `create_vegetation_task`. The system hands off the resulting task ID to a specialized monitoring agent. That secondary agent uses `get_task_result` to pull the final GeoTIFF URLs, fully traced in your OpenAI dashboard.

Cross-Reference Satellite Imagery

Cloud cover ruins optical data. When a primary scan fails, your agent can pivot immediately by calling `search_multi_dataset` to find overlapping Sentinel-2 and Landsat 8 scenes. You get exact scene IDs, cloud percentages, and download links back in your Python code. If the primary optical pass is obscured, the agent knows exactly where to look next.

Setup guide

Set up EOSDA Agriculture Satellite Data 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 EOSDA Agriculture Satellite Data tools at runtime.
3

Create your Agent

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

Run the agent

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

Pass MCPServerStreamableHttp with your endpoint URL into the mcp_servers array. Set cacheToolsList=True so your agent skips re-fetching the tool definitions on every run.

Yes. Your agent triggers the job and gets an ID back. You write a guardrail that prevents it from calling the result endpoint until a set timeout passes.

It checks standard optical and multi-spectral sensors. The agent runs the available datasets tool to see exactly what satellite feeds are active for your coordinates.

You prompt it to check the available indices first. It reads the supported list, matches the index to the crop type, and submits the correct parameter to the task creator.

Your field polygons and date ranges pass through the model to construct the API request. Do not send proprietary farm boundaries if your organization prohibits sharing geographic coordinates with third-party LLM providers.

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