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ai-room-design-mcp

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AI Room Design MCP Server

AI Room Design - Interior Visualization with AI

MCP Badge Zero Config Read Only License: MIT MCP Node

A Model Context Protocol server that exposes the canonical AI Room Design knowledge surface — image generation workflows and styles, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://airoom-design.com

🎨 About AI Room Design

AI Room Design (airoom-design.com) is a browser-based interior design tool that uses artificial intelligence to reimagine living spaces from a single photo. Users upload an image of any room, select a target style and customization options, and receive a fully redesigned visualization in seconds. The platform supports over eight room types — living rooms, bedrooms, kitchens, bathrooms, dining rooms, offices, kids' rooms, and balconies — and applies more than twenty distinct design styles powered by models including Google Gemini 2.5. A free tier with five monthly projects makes it accessible without a paid commitment, while premium plans support higher volume and batch workflows.

Related MCP server: meigenai-mcp

Key Features

  • Photo-based redesign: Upload a room photo and receive an AI-generated rendering in a chosen style, covering structural elements, furniture arrangement, flooring, and lighting.

  • 20+ design styles: Presets span Modern Minimalist, Scandinavian, Japanese Zen, Industrial, Mid-Century Modern, Bohemian, Coastal, Luxury Modern, Art Deco, and Rustic Farmhouse, among others.

  • Granular customization: Controls for floor type (wood, marble, concrete), accent lighting, and renovation depth (full renovation down to a quick refresh) let users shape how far the AI diverges from the original room.

  • Text-to-room generation: Describe a space in plain text to generate a room concept from scratch, without requiring a source photo.

  • Smart Space Planning and Intelligent Enhancement: Tools for layout suggestions and image quality improvement sit alongside the core style-transfer workflow.

  • Batch processing: Multiple rooms or property units can be processed together, reducing turnaround time for larger projects.

  • Download and share: Finished renderings export directly for use in presentations, listings, or social media.

Use Cases

  • A homeowner planning a renovation uses the tool to preview a Scandinavian living room layout before committing to new furniture purchases.

  • A real estate agent virtually stages an empty apartment with a Luxury Modern interior to improve listing photos without physical staging costs.

  • An interior designer generates a dozen concept variants for a client pitch in the time it would take to sketch one by hand.

  • A short-term rental host refreshes the look of a dated bedroom by testing different color schemes and floor types before contacting a contractor.

  • A property developer processes renderings for an entire multi-unit building in batch, producing consistent marketing visuals across all units.

Who Is It For

AI Room Design targets anyone who needs to visualize interior changes quickly and without professional design software. Homeowners exploring renovation ideas form the broadest segment — the free tier and simple four-step workflow lower the barrier for people with no design background. Real estate agents and property developers benefit from virtual staging that speeds up listing preparation. Interior designers and architects can use it as a rapid ideation layer, generating style variants to show clients before moving into detailed planning. Home staging consultants and renovation advisors will find the batch processing and export features practical for client-facing deliverables. The platform does not require design expertise; anyone comfortable uploading a photo can produce a plausible redesign.

Tools

list_styles

Return the canonical list of image-generation styles or presets the site exposes. (AI Room Design)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for AI Room Design.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for AI Room Design (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://ai-room-design/styles — Supported image-generation styles and presets.

  • site://ai-room-design/pricing — Canonical pricing entry point.

  • site://ai-room-design/faq — Short FAQ generated from public site metadata.

  • site://ai-room-design/links — Canonical URLs to share with users.

Prompts

tell_me_about_ai_room_design

Summarize what the site is, who it's for, and how it works. — AI Room Design

try_image_style_ai_room_design

Recommend a starting image-generation style for a stated goal. — AI Room Design

Installation

Install via Smithery

npx -y @smithery/cli install ai-room-design-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/ai-room-design-mcp.git
cd ai-room-design-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "ai-room-design-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/ai-room-design-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

Available Tools

3 tools
get_pricingB

Return the canonical pricing entry point for AI Room Design.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It says 'Return' implying a read operation, but does not explain what an 'entry point' means concretely (e.g., URL, price object, reference), whether it involves external calls, or what the output structure will be. This lack of behavioral detail is a notable gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that front-loads the core purpose ('Return the canonical pricing entry point'). There is no fluff, repetition, or extraneous information, and every word contributes meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and no output schema, the description is minimal but leaves the nature of the 'pricing entry point' ambiguous—it could be a URL, a price, or some other artifact. The sibling tools suggest this belongs to a group of navigation/link retrieval tools, but the description does not clarify return format or whether it supplements or overlaps with get_official_links, so completeness is adequate but has gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so per the rubric the baseline is 4. The description does not need to explain parameter meanings, and the empty schema confirms no input is required, making parameter handling unambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource ('pricing entry point') and the action ('Return'), making it clear what the tool does. It does not explicitly contrast with siblings like get_official_links, so it doesn't fully distinguish itself, but the specific reference to pricing provides adequate differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as list_styles or get_official_links. The description simply states the function without any context about conditions, prerequisites, or exclusions, so the agent gets no decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_stylesA

Return the canonical list of image-generation styles or presets the site exposes. (AI Room Design)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. 'Return' implies a read-only operation, but it does not disclose potential rate limits, authentication requirements, or output format. Basic but minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with no fluff, efficiently stating the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only list with no parameters and no output schema, the description sufficiently covers what the tool returns. The sibling context further clarifies its role.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is empty. The description does not need to explain parameters, and the baseline of 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Return') and identifies a distinct resource ('canonical list of image-generation styles or presets'), clearly distinguishing it from sibling tools like get_pricing and get_official_links.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose is clear enough to infer when to use it (when listing styles), but it does not explicitly mention alternatives or exclusion cases. Given siblings are dissimilar, the context is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedget_official_links
    • First observedget_pricing
    • First observedlist_styles
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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: listing styles, getting pricing, and providing official links. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: list_styles, get_pricing, get_official_links. This makes the naming predictable and easy to understand.

Tool Count5/5

With only 3 tools, the server is well-scoped for its informational purpose. Each tool is essential and covers a distinct piece of canonical data.

Completeness4/5

The server covers the key informational needs (styles, pricing, links) for the AI Room Design domain. Minor additional resources like FAQ or contact info could be added, but the core surface is complete.

Resources

Unclaimed servers have limited discoverability.

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