How to Use the GrainSure MCP in Pydantic AI

Q: How do I configure GrainSure with Pydantic AI?

Install the framework using pip install "pydantic-ai-slimmcp" and initialize MCPToolset with your HTTP URL. Pass this toolset to your Agent constructor to start querying silo data.

Q: Can I use Pydantic AI with GrainSure to update thresholds?

Yes, you can use updatesilosettings to modify alert thresholds. Pydantic AI guarantees that the new threshold values conform to the expected schema before invoking the server.

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Connect GrainSure MCP to Pydantic AI

Create your Vinkius account to connect GrainSure 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 GrainSure with Pydantic AI

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Validate grain level telemetry at runtime

`get_current_level` returns structural data fields like fill percentage, remaining tonnes, and height. Pydantic AI validates these incoming values against strict Python types at runtime, ensuring your agent never processes corrupt sensor readings. If a hardware glitch causes a tool to return a negative fill percentage, the framework raises a validation error instantly. This prevents your agent from making decisions based on broken telemetry.

Guard your orders using this type-safe MCP Server

`create_delivery_order` requires precise float values for tonnes and specific date strings for scheduling. This MCP Server enforces strict parameter schemas, meaning Pydantic AI will block malformed requests before they leave your environment. The framework checks the output schema of `get_order_history` to ensure tracking IDs and delivery dates are formatted correctly. Your database remains clean and free of corrupt records.

Monitor sensor health with strict schema enforcement

`get_sensor_health` outputs battery percentages and signal strength metrics that your monitoring loops rely on. Pydantic AI parses these fields into structured models, making it easy to build automated alerts when battery levels drop. The agent uses `get_silo_details` to map these health metrics to specific grain types like wheat or barley. You get predictable, type-safe telemetry without writing custom parsing code.

Setup guide

Set up GrainSure 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": {
        "grainsure-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about GrainSure MCP in Pydantic AI

Install the framework using `pip install "pydantic-ai-slim[mcp]"` and initialize `MCPToolset` with your HTTP URL. Pass this toolset to your `Agent` constructor to start querying silo data.

The framework raises a runtime validation error immediately instead of letting your agent hallucinate. This ensures your code fails loudly if a tool like `get_usage_history` returns structural anomalies.

Yes, you can use `update_silo_settings` to modify alert thresholds. Pydantic AI guarantees that the new threshold values conform to the expected schema before invoking the server.

Yes, the MCP toolset integrates with any model supported by Pydantic AI. You can switch from OpenAI to Anthropic or local models while keeping the exact same schemas for `get_days_to_empty`.

All communication runs through a zero-trust, ephemeral V8 sandbox. Your supplier details and delivery orders are never cached or logged on external servers; they flow directly to your application.

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