What satellites are covered by this integration?

The server provides access to Sentinel-2 (high resolution), Sentinel-1 (radar), Landsat 8 and 9 (historical and medium res), and MODIS (high temporal resolution).

How do I calculate the NDVI for a specific field?

Use the `create_vegetation_task` tool. You need to provide the `index_type` as 'NDVI', the `dataset_id` (e.g., 'sentinel2'), and the `aoi` (Area of Interest) coordinates in GeoJSON format.

Is the area of interest (AOI) required for searches?

For general searches, it is optional but highly recommended to narrow down results. For index calculation tasks (`create_vegetation_task`), the AOI is mandatory to define the target area.

EOSDA Agriculture Satellite Data MCP Server for LangChain 6 tools — connect in under 2 minutes

Name: EOSDA Agriculture Satellite Data
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect EOSDA Agriculture Satellite Data through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "eosda-agriculture-satellite-data": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using EOSDA Agriculture Satellite Data, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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About EOSDA Agriculture Satellite Data MCP Server

Empower your AI agent with cutting-edge remote sensing capabilities via the EOSDA Agriculture MCP server. This integration provides instant access to high-resolution satellite data from Sentinel and Landsat missions, specifically processed for precision farming. Your agent can search for imagery across global datasets, calculate vegetation indices like NDVI, EVI, and MSAVI, and monitor soil moisture trends over time. Whether you are optimizing fertilizer application, auditing crop health, or monitoring land use, your agent acts as a dedicated agronomist and remote sensing specialist through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with EOSDA Agriculture Satellite Data through native MCP adapters. Connect 6 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Satellite Imagery Search — Search Sentinel-2, Landsat 8/9, and MODIS datasets by date and area of interest.
Vegetation Indices — Trigger processing tasks for NDVI (health), EVI (biomass), and other critical indices.
Health Monitoring — Retrieve processed results to identify areas of stress or high productivity in fields.
Dataset Intelligence — Access technical specs for available satellites including resolution and revisit times.
AOI Analysis — Input GeoJSON areas of interest to get localized intelligence for specific farms or regions.

The EOSDA Agriculture Satellite Data MCP Server exposes 6 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect EOSDA Agriculture Satellite Data to LangChain via MCP

Follow these steps to integrate the EOSDA Agriculture Satellite Data MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from EOSDA Agriculture Satellite Data via MCP

Why Use LangChain with the EOSDA Agriculture Satellite Data MCP Server

LangChain provides unique advantages when paired with EOSDA Agriculture Satellite Data through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine EOSDA Agriculture Satellite Data MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across EOSDA Agriculture Satellite Data queries for multi-turn workflows

EOSDA Agriculture Satellite Data + LangChain Use Cases

Practical scenarios where LangChain combined with the EOSDA Agriculture Satellite Data MCP Server delivers measurable value.

RAG with live data: combine EOSDA Agriculture Satellite Data tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query EOSDA Agriculture Satellite Data, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain EOSDA Agriculture Satellite Data tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every EOSDA Agriculture Satellite Data tool call, measure latency, and optimize your agent's performance

EOSDA Agriculture Satellite Data MCP Tools for LangChain (6)

These 6 tools become available when you connect EOSDA Agriculture Satellite Data to LangChain via MCP:

create_vegetation_task

) for a specific area of interest. Returns a task ID that can be used with get_task_result to retrieve results. Use get_available_indices to see all available index types. Create a vegetation index calculation task (NDVI, EVI, etc.)

get_available_datasets

Use these dataset IDs for search_dataset and create_vegetation_task. Get list of available satellite datasets

get_available_indices

Use these index types with create_vegetation_task. Get list of available vegetation indices

get_task_result

Returns the processed vegetation index data, download URLs and status. Get the result of a vegetation index task

search_dataset

) within a date range and optional area of interest. Returns scene IDs, dates, cloud cover percentages and download URLs. Use get_available_datasets to see all dataset options. Search satellite imagery for a specific dataset

search_multi_dataset

g. Sentinel-2 and Landsat 8 together). Returns scenes from all requested datasets within the date range and area of interest. Search satellite imagery across multiple datasets

Example Prompts for EOSDA Agriculture Satellite Data in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with EOSDA Agriculture Satellite Data immediately.

"Find Sentinel-2 images for my farm from the last month."

"Calculate the NDVI for this area: [GeoJSON coords]."

"What is the resolution of Landsat 8 satellite data?"

Troubleshooting EOSDA Agriculture Satellite Data MCP Server with LangChain

Common issues when connecting EOSDA Agriculture Satellite Data to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

EOSDA Agriculture Satellite Data + LangChain FAQ

Common questions about integrating EOSDA Agriculture Satellite Data MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.