EOSDA MCP Server for CrewAI 12 tools — connect in under 2 minutes
Connect your CrewAI agents to EOSDA through Vinkius, pass the Edge URL in the `mcps` parameter and every EOSDA tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="EOSDA Specialist",
goal="Help users interact with EOSDA effectively",
backstory=(
"You are an expert at leveraging EOSDA tools "
"for automation and data analysis."
),
# Your Vinkius token. get it at cloud.vinkius.com
mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)
task = Task(
description=(
"Explore all available tools in EOSDA "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 12 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About EOSDA MCP Server
Connect your EOSDA Agriculture API to any AI agent and take full control of satellite-based crop monitoring, vegetation index analysis, weather tracking, and precision agriculture through natural conversation.
When paired with CrewAI, EOSDA becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call EOSDA tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
What you can do
- Field Management — List and register agricultural fields with boundaries, crop types, and planting dates
- Vegetation Indices — Calculate 17+ indices (NDVI, EVI, NDRE, MSAVI, NDMI, etc.) from Sentinel-2 and Landsat
- NDVI Time Series — Track vegetation health trends across entire growing seasons
- EVI Time Series — Monitor enhanced vegetation index for high-biomass and tropical crops
- NDMI Time Series — Monitor crop water content and irrigation needs
- Satellite Imagery — Retrieve raw satellite imagery bands from multiple satellite sources
- Weather Data — Access 20+ years of historical weather data with 1800+ parameters
- Weather Forecast — Get forecasts from 15 days to 7 months for agricultural planning
- Soil Moisture — Monitor soil moisture levels at different depths for irrigation scheduling
- Zoning Maps — Generate productivity and vegetation health zoning maps for precision agriculture
- Index Map Rendering — Create visual vegetation index maps with customizable colormaps
- Custom Field Registration — Add new fields with GeoJSON boundaries for satellite monitoring
The EOSDA MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI 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 to CrewAI via MCP
Follow these steps to integrate the EOSDA MCP Server with CrewAI.
Install CrewAI
Run pip install crewai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Customize the agent
Adjust the role, goal, and backstory to fit your use case
Run the crew
Run python crew.py. CrewAI auto-discovers 12 tools from EOSDA
Why Use CrewAI with the EOSDA MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with EOSDA through the Model Context Protocol.
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
EOSDA + CrewAI Use Cases
Practical scenarios where CrewAI combined with the EOSDA MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries EOSDA for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff
Scheduled intelligence reports: set up a crew that periodically queries EOSDA, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain EOSDA tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow
Compliance and audit automation: a compliance agent queries EOSDA against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
EOSDA MCP Tools for CrewAI (12)
These 12 tools become available when you connect EOSDA to CrewAI via MCP:
create_field
Accepts field boundary as GeoJSON polygon or coordinates, field name, crop type, and planting date. Returns the created field with ID, calculated area, and monitoring activation status. Essential for onboarding new fields into the monitoring system, expanding farm coverage, and setting up new crop seasons. AI agents should use this when users ask "add a new field for monitoring", "register this field boundary", or need to set up satellite monitoring for a new agricultural area. Register a new agricultural field for satellite monitoring
get_evi_timeseries
EVI is more sensitive in high-biomass regions and less affected by atmospheric conditions than NDVI. Returns EVI values per satellite overpass date for trend analysis. Essential for monitoring dense canopies, tropical crops, and areas with high atmospheric interference. AI agents should reference this when users ask "show me EVI trends for this field", "how is the canopy developing", or need enhanced vegetation index analysis for high-biomass crops. Get EVI time series data for enhanced vegetation monitoring over a growing season
get_fields
Returns field names, boundaries (GeoJSON polygons), area in hectares/acres, crop type, planting dates, and current growth stage information. Essential for farm management overview, field inventory, and selecting target fields for satellite analysis. AI agents should use this when users ask "show me all my fields", "list monitored fields", or need to identify available fields for vegetation index or weather queries. List all agricultural fields monitored in your EOSDA account
get_ndmi_timeseries
NDMI is sensitive to vegetation water content and is used for drought monitoring, irrigation scheduling, and fire risk assessment. Returns NDMI values per satellite overpass date. Essential for water stress detection, irrigation optimization, drought impact assessment, and harvest timing. AI agents should use this when users ask "show me crop water stress trends", "how is the moisture content changing", or need moisture index analysis for irrigation planning. Get NDMI time series data for crop water stress monitoring
get_ndvi_timeseries
Returns NDVI values per satellite overpass date, enabling trend analysis of crop health, growth stages, and stress detection. Essential for season-long crop monitoring, growth curve analysis, yield prediction, and identifying problematic periods. AI agents should use this when users ask "show me the NDVI trend for this season", "how has vegetation health changed over the growing season", or need time-series vegetation analysis. Get NDVI time series data showing vegetation health trends over a growing season
get_satellite_imagery
) for a specific field and date range. Supports Sentinel-2, Landsat 8/9, MODIS, NAIP, and CBERS-4 sources. Returns image metadata, acquisition dates, cloud cover percentages, band availability, and download URLs. Essential for visual crop assessment, custom band analysis, change detection, and downloading raw imagery for further processing. AI agents should reference this when users ask "show me satellite images of my field from last week", "get Sentinel-2 imagery for field X", or need raw satellite imagery download links. Retrieve raw satellite imagery for a specific field and date range
get_soil_moisture
Returns soil moisture levels at different depths (surface, root zone, deep soil), moisture anomalies, and irrigation recommendations. Essential for irrigation scheduling, drought monitoring, water stress detection, and water resource optimization. AI agents should reference this when users ask "what is the soil moisture level in my field", "do I need to irrigate", or need soil moisture data for irrigation planning. Get soil moisture data for agricultural fields
get_vegetation_index
Supports 17+ indices including NDVI (vegetation health), EVI (enhanced vegetation index), GNDVI (green NDVI), NDRE (red edge), MSAVI (soil adjusted), RECI (red edge chlorophyll), NDSI, NDWI (water), SAVI, ARVI, GCI (chlorophyll), SIPI, NBR (burn ratio), MSI (moisture), ISTACK, FIDET, and CCCI. Returns index values, statistics (mean, min, max, std), satellite source (Sentinel-2, Landsat), and cloud cover percentage. Essential for crop health assessment, stress detection, and growth monitoring. AI agents should use this when users ask "what is the NDVI for my corn field this month", "calculate vegetation health for field X", or need vegetation index analysis. Calculate vegetation indices (NDVI, EVI, NDRE, etc.) for a specific field and date range
get_weather_data
Includes 1800+ weather parameters: temperature (air, soil), precipitation, humidity, wind speed/direction, solar radiation, evapotranspiration, dew point, pressure, and growing degree days. Historical data available since 1979. Essential for irrigation planning, frost risk assessment, disease/pest pressure modeling, and yield prediction. AI agents should use this when users ask "what was the weather like on my field last month", "get temperature and rainfall data", or need historical weather analysis for crop management decisions. Get historical and current weather data for agricultural fields
get_weather_forecast
Includes temperature, precipitation, humidity, wind, and solar radiation forecasts. Essential for planting schedule optimization, harvest timing, irrigation planning, frost protection, and seasonal crop management. AI agents should reference this when users ask "what is the weather forecast for my field next week", "get seasonal precipitation forecast", or need forward-looking weather data for agricultural planning. Get weather forecasts (15 days to 7 months) for agricultural fields
get_zoning_map
Returns zone boundaries, average index values per zone, area percentages, and management recommendations. Essential for variable rate application (VRA), precision fertilization, targeted irrigation, and yield optimization. AI agents should use this when users ask "create a zoning map for my field", "generate productivity zones", or need management zone maps for precision agriculture. Generate productivity and vegetation health zoning maps for fields
render_index_map
Returns rendered raster images (JPEG, PNG, or GeoTIFF) with color-coded vegetation index values overlaid on field boundaries. Supports colormaps like NDVI (green-yellow-red), thermal, grayscale, and custom color schemes. Essential for field reports, stakeholder communication, visual crop assessment, and creating shareable vegetation maps. AI agents should reference this when users ask "create a color-coded NDVI map of my field", "generate a vegetation health visualization", or need shareable vegetation index images for reports. Generate visual vegetation index maps with customizable colormaps for field visualization
Example Prompts for EOSDA in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with EOSDA immediately.
"Show me the NDVI trend for my corn field over the 2025 growing season."
"What is the 15-day weather forecast and current soil moisture for my soybean field?"
"Generate a productivity zoning map for my wheat field with 4 zones."
Troubleshooting EOSDA MCP Server with CrewAI
Common issues when connecting EOSDA to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
EOSDA + CrewAI FAQ
Common questions about integrating EOSDA MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.Can different agents in the same crew use different MCP servers?
mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.What happens when an MCP tool call fails during a crew run?
Can CrewAI agents call multiple MCP tools in parallel?
process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.Can I run CrewAI crews on a schedule (cron)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect EOSDA with your favorite client
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect EOSDA to CrewAI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.