How to Use the EOSDA Agriculture Satellite Data MCP in Google ADK

Q: Can Google ADK filter the EOSDA Agriculture Satellite Data tools?

You restrict access using the toolnames filter. If you only want the agent searching metadata and not spending credits on calculations, just expose the search endpoints.

Feed raw satellite imagery into your Google ADK agents for massive-scale crop health analysis.

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Connect EOSDA Agriculture Satellite Data MCP to Google ADK

Create your Vinkius account to connect EOSDA Agriculture Satellite Data to Google ADK 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 Google ADK

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Perplexity

Scan Millions of Acres via Google ADK

Gemini models handle massive context windows, meaning you can feed them hundreds of satellite scenes at once. Connect this MCP Server to let your Google agent fire off `search_multi_dataset` requests across entire agricultural basins. The agent pulls back scene IDs, dates, and cloud cover percentages for both Landsat and Sentinel. You dump that raw metadata straight into BigQuery for your data science team to analyze.

Calculate NDVI on the Fly

Optical remote sensing requires specific index calculations to mean anything. Your Vertex AI agent uses `create_vegetation_task` to generate EVI or soil moisture metrics without you writing custom raster math. Once the satellite finishes processing, the agent grabs the download URLs via `get_task_result`. It reads the GeoTIFFs and correlates those findings with the historical yield data you already keep in Google Cloud.

Query Sensor Availability

Not every satellite covers every farm. Before committing compute resources to this MCP Server, your agent calls `get_available_datasets` to figure out which sensors actually flew over your target coordinates. It follows up with `get_available_indices` to match the right spectral bands to the problem. If you are looking for water stress, the agent knows to request moisture indices instead of basic vegetation health.

Setup guide

Set up EOSDA Agriculture Satellite Data MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with EOSDA Agriculture Satellite Data tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="EOSDA Agriculture Satellite Data_agent",
    model="gemini-2.0-flash",
    instruction="You have access to EOSDA Agriculture Satellite Data tools via MCP.",
    tools=mcp_tools,
)

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Common questions about EOSDA Agriculture Satellite Data MCP in Google ADK

Wrap your endpoint URL in StreamableHttpServerParameters and pass it to McpToolset. Assign that toolset to your LlmAgent tools array.

You restrict access using the tool_names filter. If you only want the agent searching metadata and not spending credits on calculations, just expose the search endpoints.

Gemini handles huge context blocks. You pull a massive list of overlapping satellite scenes and ask the agent to find the three best cloud-free days without hitting token limits.

Instruct your agent to check the cloud cover percentage returned by the search tool. If it exceeds your threshold, have the agent automatically request an older date range.

The server only sees the bounding boxes and timestamps your agent sends it. Google Cloud handles the internal auth, but the external API still receives the geographic coordinates of your target fields.

Use it with your favorite AI tools

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