Natural Light Estimator MCP for AI. Translating Code Minimums Into Buildable Window Specs

Q: What is the difference between classifylighting and simply checking code?

classifylighting takes the calculated lux number and assigns it an easily understandable category, like 'adequate' or 'dim.' It simplifies complex rules into immediate status feedback.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

Natural Light Estimator calculates minimum window and opening dimensions needed for a room, ensuring your design meets international building codes and provides adequate cross-ventilation.

It moves beyond basic square footage requirements by giving you actionable measurements that are structurally feasible in US and EU markets.

What your AI can do

Classify lighting

Takes an estimated lux level and tells you if the natural light is dark, dim, adequate, or bright.

Estimate light level

Calculates how much natural light a room receives based on its size, window area, and geographic location.

Recommend improvements

Provides concrete suggestions for increasing light or improving ventilation when the current conditions fall below code standards.

Calculate theoretical illumination levels

The system estimates the natural light intensity (lux) in a room based on window and room dimensions.

Determine code-compliant window sizing

It calculates specific width and height measurements required to meet minimum building standards for your area.

Assess light quality status

The MCP categorizes the calculated lux level into simple terms like 'adequate' or 'dim'.

Generate actionable design reports

You get a single report containing concrete, dimensioned window specifications for building plans.

Ask an AI about this

Included with Plan

Waiting for input…

AI Agent

Natural Light Estimator: 3 Tools Available

These tools allow you to calculate illumination levels, check compliance status, recommend fixes, and generate full architectural reports for natural light.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Natural Light Estimator on Vinkius

Classify Lighting

Takes an estimated lux level and tells you if the natural light is dark, dim, adequate, or bright.

Estimate Light Level

Calculates how much natural light a room receives based on its size, window area...

Recommend Improvements

Provides concrete suggestions for increasing light or improving ventilation when the...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Natural Light Estimator integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "natural-light-estimator": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Natural Light Estimator tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"natural-light-estimator": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Natural Light Estimator, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Natural Light Estimator MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Natural Light Estimator. 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.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

The hassle of manual building code checks

Right now, designing for light means endless back-and-forth: checking the local zoning book for minimum ratios, cross-referencing those ratios with the available wall space, and then running separate calculations to see if your theoretical openings even look structurally sound. It’s a massive amount of spreadsheet work that always leaves you wondering if you missed a key code detail.

With this MCP, you just give it the room size and tell it the codes you're working against. The system handles the entire compliance check, giving you concrete, dimensioned window specifications instead of theoretical minimums. You get specs ready to hand to an engineer.

Get Specific Dimensions with `recommend_improvements`

Before this MCP, if your light was classified as 'dim,' you had to manually guess what fix would work—more windows? Reflective paint? Where exactly should the new opening go? You were left with vague suggestions that didn't translate into blueprints.

Now, after running the initial assessments, the system uses `recommend_improvements` to provide concrete directions. It tells you precisely how much wider or taller a window needs to be to hit 'adequate,' closing the gap between theory and buildable reality.

Support 24/7 support@vinkius.com ↗

Security Vinkius Trust Center ↗

SLA Service Level Agreement ↗

Report Listing Send Report ↗

What your AI can actually do with this

Designing a space means more than just hitting required square footage; it demands natural light and proper airflow. Traditional building code calculations often give minimum area ratios, but they don't tell an architect how to build those openings into a wall. This MCP bridges that gap by connecting abstract code requirements directly to physical window dimensions.

Your agent first estimates the theoretical lux level using your room and window sizes. Next, it determines precise width and height measurements needed to meet minimum area standards for different regions. The system then classifies that light level—telling you if the space is adequate or dim—and finally compiles everything into a single report with dimensioned specifications ready for construction plans.

Because this process combines multiple complex steps (like estimating light and then recommending changes), your agent can manage these multi-step calculations using Vinkius's ability to chain different MCPs together, ensuring every calculation flows logically from one step to the next.

Built · Hosted · Managed by Vinkius Natural Light Estimator MCP - Code Compliant Window Sizing

Server ID 019ec1f1-5238-7000-94b9-5b90e5920f94

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

What Changes When You Connect

You stop guessing if a space is lit enough. The estimate_light_level tool calculates the exact lux level using room size and window area, giving you immediate data.

The system prevents costly redesigns by ensuring your proposed windows are physically possible. It translates abstract code areas into precise width/height dimensions.

You get instant feedback on compliance status with classify_lighting. Instead of manually cross-referencing codes, the MCP tells you if the light is 'adequate' or needs work.

If things aren't right, you don't start over. The recommend_improvements tool immediately suggests actionable ways to increase natural light or adjust placement.

The final report combines all these checks, giving builders a single document with dimensioned specs that satisfy the law and reality.

See it in action

01 01

Designing a new commercial office space

An architect needs to design a large open floor plan. They run an initial calculation using estimate_light_level for various window sizes. If the result is 'dim,' they pass that output to the recommend_improvements tool, which suggests specific placement changes to hit code compliance.

02 02

Retrofitting an old residential home

A homeowner wants to add a new bay window. They use the MCP to calculate if the proposed size meets minimum area requirements for their state. The system then uses classify_lighting on the results, confirming that the addition will bring the room up to 'adequate' light.

03 03

Checking compliance for a multi-story apartment complex

The project manager needs to check 50 units. They feed the unit dimensions and window specs into the MCP, which runs multiple checks and generates a single report, flagging every unit that falls below code minimums.

04 04

Optimizing natural ventilation in a classroom

A school designer needs to balance light and air flow. They use estimate_light_level first, then feed the resulting data into the MCP's ability to chain together checks, ensuring that both illumination and cross-ventilation rules are met simultaneously.

When It Fits, When It Doesn't

Use this MCP if you need to prove that a design's dimensions are both code compliant and physically buildable. You should use this when the goal is generating actionable architectural specifications. Don't use it if you just need a single, quick estimate of light intensity; for that, estimate_light_level works alone. But if your job requires figuring out what to do next—if the current design fails code—you must let the MCP chain together calculations and pass the results through both classify_lighting and then into recommend_improvements. If you only need to know what minimum area is required, a simple calculator will suffice. But if you need dimensions that actually fit in a wall, this MCP is what you need.

Questions you might have

How does Natural Light Estimator use `estimate_light_level`? +

It uses your room area, window area, and latitude to calculate the theoretical lux level of natural light. This gives you a raw measurement that tells you if the current openings are sufficient.

What is the difference between `classify_lighting` and simply checking code? +

classify_lighting takes the calculated lux number and assigns it an easily understandable category, like 'adequate' or 'dim.' It simplifies complex rules into immediate status feedback.

Can I use Natural Light Estimator to check multiple rooms at once? +

Yes. You can feed the MCP a list of room sizes and window openings, letting it cycle through all necessary calculations and generating a single report for your entire build.

What if my light level is 'dim'? How do I fix it using Natural Light Estimator? +

Run the assessment first. If the result is 'dim,' pass that output to recommend_improvements. The tool will then provide specific, actionable suggestions for increasing natural light or adjusting window placement.

How does the Natural Light Estimator secure my architectural data when running `estimate_light_level`? +

Your input measurements are never stored. Vinkius uses a zero-trust proxy for all credentials, meaning your keys pass through in transit but never sit on disk. Every tool call generates a cryptographically signed audit trail for full security.

Is the Natural Light Estimator compatible with my existing AI clients when using `classify_lighting`? +

Yes, this MCP connects through the Model Context Protocol (MCP) standard. You connect once from any client like Claude, Cursor, Windsurf, or VS Code, and you can access all tools in the catalog.

What happens if I provide impossible dimensions when calling `estimate_light_level`? +

The system checks for physical plausibility first. If your inputs are contradictory (e.g., a negative area), it won't run the calculation and will return an explicit error code, telling you exactly what needs correction.

Can I use `recommend_improvements` to plan improvements across multiple zones simultaneously? +

You can certainly chain calls together. You provide data for several areas in sequence, and the MCP handles passing that context through all related tool calls to give you a holistic recommendation.

What is the difference between minimum area and window dimensions? +

The system first calculates a 'minimum area' (e.g., 1/6 of floor space). The normalizeAreaToWindowDimensions tool then takes this abstract minimum and determines the most structurally sound width and height that achieves or exceeds that required area.

Can I use these tools for a whole building, not just one room? +

Yes. The generateRoomSpecificationReport tool accepts an array of multiple rooms and processes them sequentially. This allows you to generate a full specification report for every space in your floor plan.

What if the required minimum area is too large for standard windows? +

The normalizeAreaToWindowDimensions tool includes checks against physical feasibility. If a calculation results in dimensions that are structurally questionable or too large for typical openings, it will issue a warning flag within the final report.

Connect to your AI in seconds.

Classify lighting

Estimate light level

Recommend improvements

Natural Light Estimator: 3 Tools Available

Make your AI actually useful.

Classify Lighting

Estimate Light Level

Recommend Improvements

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

The hassle of manual building code checks

Get Specific Dimensions with `recommend_improvements`

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Designing a new commercial office space

Retrofitting an old residential home

Checking compliance for a multi-story apartment complex

Optimizing natural ventilation in a classroom

When It Fits, When It Doesn't

Questions you might have