Can my AI check the current temperature and moisture in my grain silo?

Yes! First use `get_devices` to find the device ID for your silo sensors. Then use `get_temperature` and `get_moisture` with that device ID to get current readings. Temperature above 25°C or moisture above 15% may indicate spoilage risk. For historical trends, use `get_device_telemetry` with keys=temperature,moisture to see how conditions have changed over time.

How do I detect early signs of grain spoilage using CO2 levels?

Use the `get_co2` tool to check CO2 readings from headspace sensors in your bins. Elevated CO2 levels (above 1500 ppm) indicate biological activity from mold, insects, or grain respiration — often appearing days before temperature changes. Combine with `get_alarms` to check for any active spoilage alerts. If CO2 is rising, consider turning on aeration using `set_relay_state` to ventilate the bin and reduce spoilage risk.

Can I remotely control my aeration fans based on sensor readings?

Yes! Use the `set_relay_state` tool with your device ID, relay name (e.g., "fan", "aeration"), and desired state (true for ON, false for OFF). Before activating, check current conditions with `get_temperature` and `get_moisture`, and verify weather conditions with `get_weather` to ensure outdoor conditions are suitable for aeration. For example, avoid running aeration during high humidity or rain.

AgroLog MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: AgroLog
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect AgroLog through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to AgroLog "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in AgroLog?"
    )
    print(result.data)

asyncio.run(main())

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* 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 AgroLog MCP Server

Connect your AgroLog Grain Monitoring API to any AI agent and take full control of real-time temperature monitoring, moisture tracking, CO2 spoilage detection, crop level inventory, and automated aeration control through natural conversation.

Pydantic AI validates every AgroLog tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Temperature Monitoring — Get real-time grain temperature readings from sensors in silos and bins
Moisture Tracking — Monitor grain moisture content for safe storage and drying decisions
CO2 Detection — Detect elevated CO2 levels as early warning signs of spoilage and mold growth
Crop Level Inventory — Track grain volume and silo fill levels for inventory management
Weather Station Data — Access outdoor temperature, humidity, wind speed, and rainfall data
Device Management — List all monitoring devices and view their configuration attributes
Relay Control — Remotely control fans, aeration systems, and dryers connected to AgroLog devices
Alarm Monitoring — Track active alarms and alerts for proactive grain management
Historical Telemetry — Retrieve time-series sensor data for trend analysis and reporting
Multi-Customer Management — Manage devices across multiple farms or customer organizations

The AgroLog MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI 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 AgroLog to Pydantic AI via MCP

Follow these steps to integrate the AgroLog MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from AgroLog with type-safe schemas

Why Use Pydantic AI with the AgroLog MCP Server

Pydantic AI provides unique advantages when paired with AgroLog through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your AgroLog integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your AgroLog connection logic from agent behavior for testable, maintainable code

AgroLog + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the AgroLog MCP Server delivers measurable value.

Type-safe data pipelines: query AgroLog with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple AgroLog tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query AgroLog and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock AgroLog responses and write comprehensive agent tests

AgroLog MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect AgroLog to Pydantic AI via MCP:

get_alarms

Alarms are triggered by threshold breaches (high temperature, high moisture, elevated CO2, equipment failure) and indicate conditions requiring immediate attention. Returns alarm severity (critical, warning, info), alarm type, affected device, timestamp, and acknowledgment status. Essential for proactive grain management, quality issue detection, and operational response. AI agents should use this when users ask "show me all active alarms", "what alerts have been triggered", or need alarm data for operational monitoring. Optional device_id filters alarms for a specific device. Get active and historical alarms/alerts from the AgroLog monitoring system

get_co2

Elevated CO2 levels indicate biological activity (mold growth, insect respiration, or grain respiration) and are early warning signs of spoilage before temperature changes become apparent. Returns timestamped CO2 value in ppm. Essential for early spoilage detection, grain quality monitoring, and proactive storage management. AI agents should use this when users ask "what is the CO2 level in silo 2", "check headspace gas readings for device X", or need early warning indicators of grain spoilage. Get CO2/headspace gas readings from a specific monitoring device

get_crop_level

Crop level sensors measure the grain volume or height in silos and bins, enabling inventory management and capacity planning. Returns timestamped crop level value (percentage or distance). Essential for grain inventory tracking, bin capacity management, and logistics planning. AI agents should reference this when users ask "how full is silo 4", "check crop level for device X", or need inventory data for storage management and logistics planning. Get grain crop level (volume/quantity) readings from a specific monitoring device

get_customer_devices

Returns device IDs, names, types, and status for the specified customer. Essential for multi-farm management, service provider operations, and organizational device administration. AI agents should use this when users ask "show me all devices for customer X", "list sensors for this farm organization", or need customer-scoped device inventory in multi-tenant deployments. List all monitoring devices for a specific customer/organization in multi-tenant setups

get_device_attributes

Essential for understanding device setup, sensor positioning within silos, and device management. AI agents should reference this when users ask "show me the configuration for this sensor", "what is the calibration data for device X", or need device metadata for system administration. Get configuration attributes and metadata for a specific monitoring device

get_device_telemetry

Supports custom key selection (temperature, moisture, co2, humidity, etc.) and configurable data point limits for historical analysis. Essential for trend analysis, condition monitoring over time, and creating data visualizations. AI agents should reference this when users ask "show me temperature history for device X over the last 48 hours", "get moisture trend for this sensor", or need historical telemetry data for grain management analysis. Get time-series telemetry data from a specific monitoring device with customizable keys and limits

get_devices

Returns device IDs, names, types (temperature sensor, moisture sensor, weather station, crop level monitor, headspace/CO2 sensor), labels, and current status. Essential for device inventory, system overview, and selecting specific sensors for telemetry queries. AI agents should use this when users ask "show me all sensors in my grain silo", "list monitoring devices", or need to identify available devices before querying temperature, moisture, or other telemetry data. List all AgroLog monitoring devices (temperature, moisture, weather sensors) in your system

get_moisture

Moisture content is the most critical factor for safe grain storage — high moisture leads to mold, spoilage, and heating. Returns timestamped moisture value as percentage. Essential for grain quality assessment, drying decisions, and storage safety monitoring. AI agents should reference this when users ask "what is the moisture level in bin 5", "check grain moisture for device X", or need moisture data for storage management and drying planning. Get current grain moisture readings from a specific monitoring device

get_temperature

Temperature is critical for detecting spoilage, mold growth, and insect activity in stored grain. Returns timestamped temperature value in Celsius. Essential for grain quality monitoring, spoilage prevention, and ventilation scheduling. AI agents should use this when users ask "what is the temperature in silo 3", "check grain temperature for device X", or need current temperature data for storage management decisions. Device IDs can be found using get_devices. Get current grain temperature readings from a specific monitoring device

get_weather

Essential for drying decisions (outdoor air conditions for natural air drying), harvest planning (rain forecasts, wind conditions), and understanding environmental impact on stored grain. Returns the latest 10 readings with timestamps. AI agents should use this when users ask "what are the current weather conditions at my facility", "show me wind speed and rainfall data", or need weather context for grain management decisions. Get weather station data (temperature, humidity, wind, rainfall) from a specific device

set_relay_state

Accepts device ID, relay name, and desired state (true=on, false=off). Essential for remote grain management, automated ventilation scheduling, and responding to temperature/moisture alerts. AI agents should use this when users ask "turn on the fan for silo 3", "activate aeration for bin 2", or need to remotely control ventilation equipment based on sensor readings. WARNING: Always verify current conditions before changing relay states. Control relay outputs (fans, aeration, dryers) connected to an AgroLog device

Example Prompts for AgroLog in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with AgroLog immediately.

"Check the temperature and moisture in silo 3 and tell me if there is any spoilage risk."

"Show me all active alarms in my grain storage facility."

"What is the current crop level inventory across all my grain bins?"

Troubleshooting AgroLog MCP Server with Pydantic AI

Common issues when connecting AgroLog to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

AgroLog + Pydantic AI FAQ

Common questions about integrating AgroLog MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your AgroLog MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.