How to Use the Room Proportion Checker MCP in LangChain

Build complex spatial analysis chains with LangChain. Connect reasoning agents to the Room Proportion Checker.

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Connect Room Proportion Checker MCP to LangChain

Create your Vinkius account to connect Room Proportion Checker to LangChain — 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Multi-step design validation.

Your agent can build a multi-step chain using this MCP. It doesn't just run one tool; it coordinates them. For instance, you might first send room dimensions to `calculate_axis_balance_ratio` to get a preliminary harmony score. Then, your agent uses that output to decide if the proportions need immediate help. If they do, it calls `classify_room_proportions`. This lets you build full reasoning pipelines where the tool outputs become inputs for the next step.

Assess overall spatial efficiency.

Need to check if a room is just big enough? You can pass dimensions and use `evaluate_floor_area_index`. This checks how surface area stacks up against the ceiling height, giving you a core metric for space. It defaults to 2.5m if you forget the height. Because this MCP supports multiple tool calls in sequence, your LangChain agent can combine this index check with ratio calculations to validate an entire architectural plan.

Directly test structural compliance.

The Room Proportion Checker helps you confirm if a space meets basic ergonomic standards. You don't just get a grade; you get actionable data points on balance and size. Your agent treats this MCP like any other API, allowing it to validate design requirements alongside calls to databases or messaging tools in one single workflow.

Setup guide

Set up Room Proportion Checker MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Room Proportion Checker tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "room-proportion-checker-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Room Proportion Checker transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Room Proportion Checker. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Room Proportion Checker MCP in LangChain

You treat this MCP just like any other tool adapter. Your agent figures out what inputs are needed (width, length, height) and calls the correct function in sequence. This keeps your entire reasoning process contained and traceable.

Absolutely. You're not limited to simple checks. You can build chains where a classification failure triggers a balance ratio check, which then informs the final output—all in one run.

It deals with geometric measurements: room dimensions (length, width, height). The results are structured JSON objects containing ratios and classification states. It's all about the raw numbers.

You can wrap the MCP call inside a loop or a parallel graph step in your agent definition. The framework handles batching the tool calls so you don't have to write repetitive code.

The tool call itself is fast, but because it's part of a larger chain, the total latency depends on how many tools your agent calls and how complex the reasoning path is.

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