How to Use the Grain Watch MCP in Pydantic AI

Q: How do I configure the Grain Watch toolset in Pydantic AI?

Import MCPToolset from the Pydantic AI library and point it to your Vinkius MCP endpoint. Pass this toolset instance inside the toolsets list when instantiating your Agent.

Q: How do I fetch temperature trends using Grain Watch and Pydantic AI?

Your agent calls gettemperaturehistory with an optional days parameter. The historical temperature logs are returned as a validated list of float values matched to ISO timestamps.

Q: What happens if a grain silo has no active alerts?

Calling getalerts returns an empty list or a clean status object. Your agent verifies this against the expected schema and safely concludes that the silo requires no immediate attention.

Validate raw silo sensor data at runtime by connecting Grain Watch as an MCP Server to your type-safe Pydantic AI agents.

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Connect Grain Watch MCP to Pydantic AI

Create your Vinkius account to connect Grain Watch to Pydantic AI 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 Grain Watch with Pydantic AI

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Type-safe hotspot detection with Pydantic AI

`get_hotspot_alerts` returns structured sensor alarms, ensuring every temperature differential and zone location matches your expected Pydantic AI models. If a sensor returns a malformed reading, your agent catches the validation error before executing bad logic. This strict validation prevents your Pydantic AI agent from acting on corrupted telemetry data. Using this MCP Server ensures that only clean, verified alerts trigger your automated fan systems.

Validate spoilage risk calculations

`get_spoilage_risk` provides a structured risk assessment including predicted days to spoilage and contributing factors. Your Pydantic AI agent parses this output against type-safe models to guarantee the risk level is a valid enum value. The agent then cross-references this risk with `get_silo_details` to confirm the grain type matches the expected Pydantic AI storage schemas. This prevents your agent from applying corn-specific drying thresholds to wheat storage.

Monitor sensor health with strict schemas

`get_sensor_health` outputs the operational status, battery levels, and last communication timestamps for every sensor in the network. Your Pydantic AI runtime validates this payload to ensure no null values slip into your hardware status reports. If a sensor is marked offline, the Pydantic AI agent reads `get_sensor_map` to find its exact physical depth and zone. This type-safe MCP Server workflow guarantees your maintenance logs remain accurate and free of empty fields.

Setup guide

Set up Grain Watch MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "grain-watch-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Grain Watch tools.",
)

result = await agent.run("List recent Grain Watch transactions")
print(result.output)

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Common questions about Grain Watch MCP in Pydantic AI

Import `MCPToolset` from the Pydantic AI library and point it to your Vinkius MCP endpoint. Pass this toolset instance inside the `toolsets` list when instantiating your Agent.

Yes. Every schema returned by the 12 tools is automatically validated against Pydantic models, protecting your agent from processing unexpected or corrupted sensor payloads.

Your agent calls `get_temperature_history` with an optional days parameter. The historical temperature logs are returned as a validated list of float values matched to ISO timestamps.

Calling `get_alerts` returns an empty list or a clean status object. Your agent verifies this against the expected schema and safely concludes that the silo requires no immediate attention.

All silo physical dimensions, grain types, and sensor layouts are processed in isolated, ephemeral sandboxes. Vinkius secures these data streams using a single-token authentication layer that prevents unauthorized access to your hardware telemetry.

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