How to Use the Wiagro MCP in LangChain

Q: Can LangChain handle historical data from Wiagro?

Absolutely. Tools like getco2history and gethumidityhistory provide time-series data that your agent can consume sequentially. You can track trends, not just snapshot readings, which is key for predicting quality loss.

Q: If I need to know about my facility status using Wiagro and LangChain?

You use the getfacilityoverview tool first. This gives a high-level summary of all units, which you can then feed into other functions like getsilobags to drill down into specific unit details.

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Connect Wiagro MCP to LangChain

Create your Vinkius account to connect Wiagro to LangChain 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 LangChain

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Perplexity

Run multi-step checks with LangChain

Start by checking the overall facility status using `get_facility_overview`. If that suggests a problem area, your agent can next call `get_alerts` to filter for specific critical warnings in a silobag. This sequence of tool calls lets you build complex reasoning pipelines where one data point dictates the order and type of the next.

Analyze spoilage trends using LangChain

To figure out if mold is developing, you'll need more than a single reading. First, call `get_co2_history` to track biological activity over time. Then, pass that data to `get_temperature_history`. By chaining these two MCP Server outputs, your agent can pinpoint exact hot spots and predict spoilage risks based on changing CO2 levels.

Monitor structural integrity with LangChain

The system needs a full picture to report correctly. Begin by listing all monitored units using `get_silobags`. Then, if you're worried about physical damage, use `get_rupture_alerts` and filter it by the ID received from the initial list. This chain lets your agent confirm structural health across dozens of silos in one logical flow.

Setup guide

Set up Wiagro MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Wiagro tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "wiagro-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Wiagro transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Wiagro MCP in LangChain

LangChain treats every MCP tool call as a link in a usable chain. You don't just get data; you get the ability to use that output—like an alert severity—to decide which subsequent function, such as `get_quality_assessment`, needs to run next.

You'll get immediate warnings about temperature, humidity, CO2 spikes, and structural issues. The `get_alerts` tool gives you severity levels (critical, warning) right away, so your agent knows which problems need attention first.

Absolutely. Tools like `get_co2_history` and `get_humidity_history` provide time-series data that your agent can consume sequentially. You can track trends, not just snapshot readings, which is key for predicting quality loss.

You use the `get_facility_overview` tool first. This gives a high-level summary of all units, which you can then feed into other functions like `get_silobags` to drill down into specific unit details.

This server handles temperature, humidity, CO2 levels (ppm), quality scores, and structural rupture alerts. All of this operational monitoring data is exposed through the MCP Server.

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