How to Use the EOSDA Agriculture Satellite Data MCP in AutoGen

Q: How does an agent know what kind of analysis to run?

You can have an agent call getavailableindices first. It will get a list of valid indices like 'NDVI' or 'EVI'. The agent can then use this information to make an informed decision or present the options to other agents in the group.

Let multiple AutoGen agents debate satellite imagery options to make smarter, consensus-driven decisions on crop analysis.

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Connect EOSDA Agriculture Satellite Data MCP to AutoGen

Create your Vinkius account to connect EOSDA Agriculture Satellite Data to AutoGen 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 AutoGen

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Perplexity

Debate Imagery Before Analysis

Don't just run a task. Let your agents decide if it's worth running. You can have an 'Analyst' agent propose using `create_vegetation_task` on a new image. But a 'Cost' agent can first use `search_dataset` to check the image's metadata and argue that the 60% cloud cover makes the analysis a waste of money. This conversational approach uses real data to improve decisions. The agents can negotiate and agree to wait for a clearer image, saving you API credits and ensuring you only pay for useful results. It's a team of experts in a box.

Divide and Conquer Complex Searches

Assign different roles to your agents to tackle a problem from multiple angles. One agent can be in charge of finding high-resolution optical imagery using `search_dataset` with the 'sentinel-2' source. A second agent could be responsible for finding images from a different sensor platform using the same tool but a different dataset ID. After they both present their findings, a 'Lead Agronomist' agent can compare the results—cloud cover, resolution, and date—to decide which image is best suited for the user's ultimate goal. This mirrors how a real-world team collaborates.

Build a Self-Correcting Analysis Team

AutoGen conversations create a feedback loop. An agent kicks off a job with `create_vegetation_task` and gets a task ID. A separate 'Monitor' agent's only job is to repeatedly call `get_task_result` and report the status. If the task fails, the Monitor agent announces it to the group. The other agents can then discuss the failure. Maybe they'll decide to retry, or maybe they'll use `search_dataset` to pick a different source image and start a whole new task. This makes your system more resilient.

Setup guide

Set up EOSDA Agriculture Satellite Data MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes EOSDA Agriculture Satellite Data tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="EOSDA Agriculture Satellite Data_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent EOSDA Agriculture Satellite Data data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="EOSDA Agriculture Satellite Data_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent EOSDA Agriculture Satellite Data data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by EOSDA. 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 EOSDA Agriculture Satellite Data MCP in AutoGen

You create specialized agents. For instance, a 'Scout' agent uses `search_dataset` to propose an image, while a 'QA' agent checks its cloud cover percentage. They discuss the findings before a 'Manager' agent gives the final approval to call `create_vegetation_task`.

Yes, that's a perfect use case. An agent finds the best scene ID using `search_dataset`. It then passes that ID to another agent in the conversation, whose job is to take that ID and call `create_vegetation_task` to start the analysis.

A solid pattern is a three-agent team: a 'Finder' that uses `search_multi_dataset`, an 'Analyst' that uses `create_vegetation_task` on the Finder's best option, and a 'Reporter' that uses `get_task_result` to fetch the final data and present it to the user.

You can have an agent call `get_available_indices` first. It will get a list of valid indices like 'NDVI' or 'EVI'. The agent can then use this information to make an informed decision or present the options to other agents in the group.

The MCP Server doesn't log your requests. The main consideration with AutoGen is that the agents' entire conversation is often logged, including the inputs and outputs of their tool calls. This means your field's GeoJSON polygons from a `search_dataset` call could be in those logs. Securing access to the agent conversation history is critical.

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