How to Use the Centaur Analytics MCP in Pydantic AI

Use Centaur Analytics with Pydantic AI to get type-safe, validated grain sensor data directly into your Python agent workflows.

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Connect Centaur Analytics MCP to Pydantic AI

Create your Vinkius account to connect Centaur Analytics 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 Centaur Analytics with Pydantic AI

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Type-safe grain insights for Pydantic AI

Every response from `get_spoilage_predictions` is validated against your Pydantic models. You get clean, typed data every time your agent runs a check. If the API returns unexpected values, the agent fails immediately. This prevents bad data from corrupting your grain quality reports.

Real-time alert validation

Your agent calls `get_alerts` to watch for critical events. Because you use Pydantic AI, the alert severity and bin IDs are guaranteed to match your expected schema. This makes your automated response logic bulletproof. You avoid handling malformed data in your production environment.

Predictive storage modeling

Query `get_quality_forecast` to get future-looking grain data. Your agent consumes these projections to help you decide when to move or sell your grain. It uses the validated data to build reliable reports. You can trust the output because the structure is enforced at runtime.

Setup guide

Set up Centaur Analytics 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": {
        "centaur-analytics-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Centaur Analytics MCP in Pydantic AI

It ensures that every piece of sensor data is validated against a strict schema. You avoid silent errors and data corruption in your agent.

The server provides consistent, structured output. You define the Pydantic models, and the agent enforces them for every tool response.

Yes. The setup supports both Streamable HTTP and SSE, giving you flexibility in how your agent connects to the server.

The server follows standard error protocols. Pydantic AI catches these, allowing your agent to handle failures gracefully.

Access is restricted via your endpoint token. All data transmission is encrypted, ensuring your private sensor readings remain confidential.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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