How to Use the Room Proportion Checker MCP in LlamaIndex

Index spatial data for RAG applications with LlamaIndex. Ground your knowledge base using Room Proportion Checker outputs.

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Connect Room Proportion Checker MCP to LlamaIndex

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

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Store architectural validation results.

Run a check on a building plan, then index the output. You can store the classification result from `classify_room_proportions` alongside the original blueprints. This makes your knowledge base searchable. Later, when a user asks about zoning compliance, LlamaIndex pulls that exact structural report into the context window for grounding.

Search historical dimension reports.

Instead of relying on memory or hallucinations, you index the raw data output from `calculate_axis_balance_ratio`. This means your application can answer questions like, 'What was the harmony score for the East Wing in Q3?' The MCP output becomes a verifiable source document that LlamaIndex uses to construct its answers.

Query spatial efficiency metrics.

Use `evaluate_floor_area_index` on critical room data, then index the result. This allows you to build specific RAG queries focused only on physical space limitations or efficiency scores. Your developer can combine this with document retrieval to create a single queryable knowledge index: 'Find all rooms where FAI was below 1.5 and the balance ratio was low.'

Setup guide

Set up Room Proportion Checker MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Room Proportion Checker MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Room Proportion Checker tools.",
)
response = await agent.run("List recent Room Proportion Checker data")

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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 LlamaIndex

It takes the structured output (the JSON) from the MCP tools and embeds it into your vector store. This means you can search by concept, not just keyword, finding reports based on 'poor harmony' or 'low FAI'.

Yes. Once the output is indexed, your application treats it like any other document—you can query historical data points across months and projects.

The server handles geometric measurements (L, W, H). When you index the results, you are saving structured numeric data like ratios and indices, making them precise for retrieval.

You simply re-run your ingestion pipeline. The MCP output is treated as a delta or an update record, enriching the existing knowledge graph with fresh validation reports.

It deals exclusively with geometric measurements and derived ratios (like axis balance ratio). The index stores these numbers alongside contextual metadata from your documents.

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