How to Use the Room Proportion Checker MCP in Pydantic AI

Q: What happens if I omit the height when running Room Proportion Checker with Pydantic AI?

The evaluatefloorareaindex tool handles it automatically, substituting a 2.5-meter default and ensuring that even partial data results in a perfectly structured index score.

Validate structural data with your Pydantic AI agent; guaranteed correct output every time.

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Connect Room Proportion Checker MCP to Pydantic AI

Create your Vinkius account to connect Room Proportion Checker to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Room Proportion Checker with Pydantic AI

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Type-Safe Dimension Checks for Pydantic AI

Pydantic's strength is correctness. When you run `calculate_axis_balance_ratio`, the MCP ensures the output adheres to a strict schema, validating your results before your agent even sees them. You don't get unexpected data types. This means if the API returns something weird, your agent fails loud with a validation error—no silent corruption.

Structured Classification via Pydantic AI

The MCP uses `classify_room_proportions` to categorize the space. Because this toolset is designed for Pydantic, the classification result will always be a predictable field (e.g., 'Well-Proportioned'), which your agent can trust instantly. This level of type safety eliminates guesswork when building complex decision trees into your production pipeline.

Guaranteed Index Output with Pydantic AI

Need to assess spatial efficiency? Use `evaluate_floor_area_index`. If the input data is missing height, the MCP uses a 2.5-meter default and ensures the final index score fits your defined output model. It's all about reliable structure; you know exactly what format the data will take when it hits your agent.

Setup guide

Set up Room Proportion Checker 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": {
        "room-proportion-checker-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Room Proportion Checker transactions")
print(result.output)

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Common questions about Room Proportion Checker MCP in Pydantic AI

The system processes raw room dimensions and guarantees that every piece of resulting data—whether a score or a classification—fits a predictable Python model. This means your agent's logic always receives clean, typed inputs.

Since the MCP is model-agnostic and runs through Vinkius, you can connect it to any LLM—OpenAI, Anthropic, or Gemini. The only thing that matters is the guaranteed schema validation.

The `evaluate_floor_area_index` tool handles it automatically, substituting a 2.5-meter default and ensuring that even partial data results in a perfectly structured index score.

Yes, reliability is its whole point. The runtime validation means if the underlying API call produces unexpected or corrupt data, your agent fails immediately and loudly, pointing out the exact schema violation.

The server only deals with geometric dimensions—length, width, height, surface area, and derived indices. No personal identifiers or sensitive records are ever processed.

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