How to Use the Agro MCP in CrewAI

Q: Can I build a fully autonomous crop monitoring system with Agro and CrewAI?

Yes. You can create a CrewAI team where one agent uses getcurrentweather to monitor conditions, and another uses getndvihistory to track crop health. If they detect an issue, they can pass the task to a third agent to send a notification.

Q: What's a good agent structure in CrewAI for analyzing a new farm with the Agro server?

A good start is a sequential crew. Agent 1 uses createpolygon. Agent 2 uses the new polygon ID to run gethistoricalweather and gethistoricalsoil. Agent 3 summarizes the findings. This turns a complex task into a simple, repeatable process.

Deploy a crew of autonomous farm analysts with CrewAI. Assign roles for monitoring, analysis, and reporting using the Agro toolset.

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Connect Agro MCP to CrewAI

Create your Vinkius account to connect Agro to CrewAI 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 Agro with CrewAI

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Assign Agents to Watch Your Fields

Build a team of agents that work together. Assign a 'Scout' agent the `get_current_weather` and `get_current_soil` tools. Give an 'Analyst' agent `get_ndvi_history` and `get_historical_weather`. They can work in parallel, constantly monitoring a set of fields. CrewAI lets you define these roles and their specific tools. The Scout finds anomalies, and the Analyst digs deeper into historical data to find the cause. They share their findings through CrewAI's shared memory, creating a complete picture without manual intervention.

Create Research and Analysis Pipelines

Set up a sequential crew. The first agent uses `list_polygons` and `search_imagery` to gather raw data and satellite photos for a region. It then passes its findings to a second agent. The second agent, an analyst, takes that data and uses `get_historical_soil` and `get_accumulated_temperature` to write a detailed report on soil health and growth conditions. This is how you automate complex EOD or weekly reporting with this MCP server.

Build an Escalation Path with Agents

CrewAI's hierarchical structure is perfect for risk management. A junior 'Field Agent' can only use read-only tools like `get_polygon` and `get_forecast_uvi`. It reports its findings to a 'Manager' agent. Only the Manager agent has access to tools that modify data, like `update_polygon` or `delete_polygon`. This creates a clear chain of command and prevents a rogue agent from making unauthorized changes, letting you build safer autonomous systems.

Setup guide

Set up Agro MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Agro tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Agro Analyst",
    goal="Access and analyze Agro data via MCP.",
    backstory="Expert analyst with direct Agro access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Agro transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Agro Analyst",
        goal="Access and analyze Agro data via MCP.",
        backstory="Expert analyst with direct Agro access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Agro transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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Common questions about Agro MCP in CrewAI

Yes. You can create a CrewAI team where one agent uses `get_current_weather` to monitor conditions, and another uses `get_ndvi_history` to track crop health. If they detect an issue, they can pass the task to a third agent to send a notification.

When defining your agents in CrewAI, you can pass a filtered list of tools to each one. For example, give your 'researcher' agent `search_imagery` and your 'analyst' agent `get_historical_soil`. This enforces role specialization.

A good start is a sequential crew. Agent 1 uses `create_polygon`. Agent 2 uses the new polygon ID to run `get_historical_weather` and `get_historical_soil`. Agent 3 summarizes the findings. This turns a complex task into a simple, repeatable process.

Exactly. That's a core CrewAI pattern. An 'Onboarding' agent can use the `create_polygon` tool. It then passes the new polygon ID to an 'Analysis' agent via shared memory, which can then immediately use tools like `get_ndvi_history` on that new field.

The only data processed is what your agents explicitly send in tool calls, like polygon coordinates or date ranges for historical data. Vinkius ensures each request is handled in a separate, temporary sandbox. Your API token is the only credential, and no farm or field data is ever stored by the server.

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