How to Use the Wiagro MCP in AutoGen

Q: Can AutoGen handle facility-wide monitoring with Wiagro?

Yes. You can assign an 'Executive Agent' to run getfacilityoverview and then have specialized agents challenge that summary using granular data, like querying individual silobag details via getsilobagdetails.

Q: If I need to know about historical humidity using Wiagro and AutoGen?

The agents can compare the long-term trend from gethumidityhistory against current best practices. They might challenge a current storage plan if the historical data shows recurring condensation patterns.

Achieve consensus on complex decisions with AutoGen and Wiagro's MCP Server.

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Connect Wiagro MCP to AutoGen

Create your Vinkius account to connect Wiagro 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 Wiagro with AutoGen

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Perplexity

Determine optimal action using AutoGen

You can set up a debate between a 'Safety Agent' and an 'Operations Agent.' The Safety Agent calls `get_rupture_alerts` to flag immediate physical risks. Meanwhile, the Operations Agent uses `get_quality_assessment`. They negotiate a final recommended action based on both structural integrity and quality decline.

Evaluate storage conditions with AutoGen MCP Server

A multi-agent system excels at complex checks. One agent queries `get_current_readings` for immediate data, while another calls `get_satellite_data` to bring in external weather context. The agents debate the risk profile before giving a final storage recommendation.

Manage sensor network with AutoGen

When you need to know if your sensors are reliable, let two agents compare notes. One agent uses `get_sensor_health` to check battery life and signal strength. The second agent cross-references that data against the unit list from `get_silobags`, flagging units where sensor failure might hide a critical problem.

Setup guide

Set up Wiagro 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 Wiagro 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="Wiagro_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Wiagro 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="Wiagro_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Wiagro data")
    print(result.messages[-1].content)

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Common questions about Wiagro MCP in AutoGen

AutoGen allows specialized agents to access and debate the results of multiple MCP tool calls. Instead of one simple query, you get a consensus decision based on different perspectives using Wiagro data.

The system handles critical operational debates: Should we evacuate? The agents compare `get_alerts` (operational warnings) against `get_temperature_history` (spoilage heating). They debate the urgency and recommend a final action.

Yes. You can assign an 'Executive Agent' to run `get_facility_overview` and then have specialized agents challenge that summary using granular data, like querying individual silobag details via `get_silobag_details`.

The agents can compare the long-term trend from `get_humidity_history` against current best practices. They might challenge a current storage plan if the historical data shows recurring condensation patterns.

The server touches temperature, humidity, CO2 levels (ppm), structural rupture alerts, sensor health status, and detailed silobag metadata. This rich mix lets the agents simulate expert deliberation.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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