Blender integration through the Model Context Protocol
Nothing to run from here
Luw.ai is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
100/100
trust score
16
downloads a day
local
or remote
268
downloads in 30d
What it does
AI interior, exterior and landscape design, photoreal rendering, image editing, video and 3D.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --env 'LUW_API_KEY=${LUW_API_KEY}' --transport stdio luw-ai-mcp '--' npx -y @luw-ai/mcp
# 2 — ask your agent for something these tools do
# luw_interior_design, luw_exterior_design, luw_sketch_to_render and 19 more
It needs LUW_API_KEY: this reads it from your environment, so export it first.
Luw.ai costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
LUAll 1 server
luw1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one can run either way — at an address its publisher runs, or as a process under your own user, with your files and your network — and this marketplace is in the path of neither.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. Claude Code reads LUW_API_KEY (Your Luw.ai API key from https://app.luw.ai/dashboard/api) from your environment each time it starts the server, so the value stays out of its config: export it in your shell profile (macOS, Linux), or run setx LUW_API_KEY … and open a new terminal (Windows). The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user --env 'LUW_API_KEY=${LUW_API_KEY}' --transport stdio luw-ai-mcp '--' npx -y @luw-ai/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Or connect to its publisher’s address
Its publisher also runs it at https://mcp.luw.ai/mcp: nothing to start on your machine, and this marketplace is not in the path.
claude mcp add --scope user --transport http luw-ai-mcp https://mcp.luw.ai/mcpFree on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
It reaches a system of yours, so it needs your own credential rather than the publisher’s: LUW_API_KEY — Your Luw.ai API key from https://app.luw.ai/dashboard/api. You set it in your own client’s config, on your machine — this marketplace never holds it.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
22 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Redesign an interior photo — home, office, shop, hotel — in any style while keeping the room's architecture.
No parameter schema on file.
Redesign a building exterior or facade photo — houses, villas, apartments, commercial buildings — in a new architectural style,
No parameter schema on file.
Turn a hand sketch, line drawing, floor-plan perspective or rough draft into a photorealistic render. Costs 1 credit per variation.
No parameter schema on file.
Turn a 3D model view, CAD/BIM screenshot, clay render or basic visualization into a photorealistic architectural render. Costs 1 credit per variation.
No parameter schema on file.
Edit any image with a plain-language instruction: "make the sofa green velvet", "add a pendant lamp over the table", "turn it into a night scene".
No parameter schema on file.
Change only a masked area of an image. Give a prompt to add/replace what's there, remove=true to erase it, or material_image to re-surface it
No parameter schema on file.
Design a garden, yard or outdoor area inside a masked region of a photo, with plants chosen for the location's climate and sun exposure. Costs 1 credit.
No parameter schema on file.
One-step image utilities, 1 credit each:
No parameter schema on file.
Cut out a product/object from its background (transparent PNG) — or, with a prompt, place it in a new generated scene for marketing shots
No parameter schema on file.
Detect objects in a photo and return black-and-white masks as image URLs — every object (wall, floor, sofa, …) or only what you describe in prompt.
No parameter schema on file.
Generate a photorealistic image or illustration from a text prompt (optionally guided by an input image) — concept art, interiors, products, marketing visuals.
No parameter schema on file.
Generate a seamless, tileable pattern or texture — tiles, wallpaper, fabric, terrazzo, wood, stone — ready to repeat across a surface. Costs 1 credit.
No parameter schema on file.
Animate a design image into a short cinematic video — fly-throughs, dolly moves, drone shots, reveals.
No parameter schema on file.
Generate a textured 3D model (GLB) from a photo of an object — furniture, decor, products. Add up to 3 photos from other angles for better accuracy.
No parameter schema on file.
Ask Luw.ai's ArchiGPT, an AI architect: design advice, feng shui, space planning, material and color suggestions, technical questions,
No parameter schema on file.
Collect the result of a Luw.ai generation that was still processing (tools return a processing_url when a job outlasts their wait).
No parameter schema on file.
Upload an image, video or 3D file to Luw.ai storage and get a URL usable by every Luw.ai tool.
No parameter schema on file.
Look up valid values for other Luw.ai tools: design_styles (styles param), interior_types (room_type), exterior_types (building_type),
No parameter schema on file.
Call POST /generate with raw API parameters, for models or options the dedicated tools don't cover (e.g. persona-slot inputs, webhooks).
No parameter schema on file.
Personas hold a reusable design identity: style knowledge plus saved images (trainings). Slots 1-6 train the persona's visual style
No parameter schema on file.
Organize work in Luw.ai projects (called boards in the API), with nested folders and media.
No parameter schema on file.
Enterprise team management: plan and credit balance, per-member usage, members, roles and invitations. Some actions need admin/owner rights.
No parameter schema on file.
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Luw.ai or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 100 / 100
Scanned: 0.3.2 · 14 Oct 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score100
Findings1 threat · 0 capability
Capabilityminimal
Coveragesource
Verificationsource
Surface digest36a4abf5e5bb612c343b0ddd2faf692c888365e957d070b1bb8752f317631501
Score vectorthreat 100 → client 100
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| verification-discount | source | publisher verification (provenance) — cryptographic build provenance ties the artifact to its source | −0 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfacenot available
Implementation sourceread
Package metadataread
Live transportnot available
No tools were enumerated, so prompt-injection, capability and toxic-flow analysis had no tool surface to inspect. To grade a package’s real runtime tools, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| No per-tool rows for this release — see the note below. | ||
No tool surface was enumerated, so there is nothing to attribute a capability to.
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digest36a4abf5e5bb612c
Scanned2026-10-14
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 0.3.1. Pin a release below and the command asks for exactly that one.
0.3.1
13 Oct 2026
latestNo release note was published with this version.
0.2.0
10 Oct 2026
No release note was published with this version.
0.1.0
undated
- The version this listing was on when mcprush began following its releases. The registry was not asked when it shipped, so this row carries no date.
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no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish Luw.ai can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.