How to Use the Room Proportion Checker MCP in Mastra AI

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Connect Room Proportion Checker MCP to Mastra AI

Create your Vinkius account to connect Room Proportion Checker to Mastra 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 Mastra AI

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Workflow-Driven Proportion Validation

You can build complex, multi-step workflows using your agent that requires multiple checks. The MCP client handles the sequence: first, it runs `calculate_axis_balance_ratio` to get a symmetry score. Then, if the score is low, it automatically calls `classify_room_proportions`. This makes sure necessary corrections are flagged. If the initial check fails, Mastra AI's built-in retries can attempt to re-evaluate with slightly adjusted inputs, ensuring data integrity for critical design reviews.

Handling Missing Room Data

When assessing spatial efficiency, you use `evaluate_floor_area_index`. If the user forgets to provide a height dimension, the MCP automatically defaults to 2.5 meters. This prevents workflow failure and keeps your process moving forward. Mastra AI manages these requirements gracefully, letting you focus on building the logic rather than managing edge case errors.

Human-in-the-Loop Approvals

For high-stakes designs, use the `classify_room_proportions` tool and implement a human approval step. This ensures that even if your agent determines a room is 'well-proportioned,' a reviewer must explicitly approve it before proceeding to the next stage of construction planning. This structured approach minimizes risk by forcing manual confirmation on key architectural decisions.

Setup guide

Set up Room Proportion Checker MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Room Proportion Checker tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "room-proportion-checker-mcp-client",
  servers: {
    "room-proportion-checker-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Room Proportion Checker Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Room Proportion Checker tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Room Proportion Checker transactions"
);
console.log(result.text);

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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 Mastra AI

You define a workflow that calls the necessary tools. Mastra AI handles the execution, running the checks and managing any required retries if the initial analysis fails.

Yes. You can set up automatic retries with exponential backoff. If a proportion calculation fails due to transient errors, the agent tries again using different parameters.

It requires dimensional input (L, W, H) for full analysis. If you omit height, it uses 2.5m as the default to keep your workflow running smoothly.

You can chain them together in one complex workflow. For instance, you'll calculate the ratio and then classify the proportions based on that initial score.

The server touches raw dimensional measurements, specifically length, width, and height (L, W, H). This geometric data is used to assess structural viability.

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