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Photorealistic render (Render AI)

luw_render

Luw.ai Render AI: turn a 3D model view, CAD/BIM screenshot, clay render or basic visualization into a photorealistic architectural render. Costs 1 credit per variation.

Instructions

Luw.ai Render AI: turn a 3D model view, CAD/BIM screenshot, clay render or basic visualization into a photorealistic architectural render. Costs 1 credit per variation.

Input Schema

NameRequiredDescriptionDefault
imageYesThe 3D view or base render (https:// URL, local file path, or data: URI).
engineNoLuw.ai model: aria (default) or symphony (Symphony-3).
formatNoOutput image format.
promptNoWhat you want, in plain language.
precisionNoHow strictly to keep the input's structure: 90 = precise, 75 = balanced, 40 = creative.
variationsNoNumber of alternative designs (1-4, default 1); each is billed as a generation.
reference_imagesNoUp to 6 reference images (furniture, materials, products, mood boards) to draw from.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, openWorldHint=true and destructiveHint=false, so the mutation profile is covered externally. The description adds the billing rule ('Costs 1 credit per variation'), which is genuinely useful and not in the annotations, but it says nothing about latency, async behavior, or how generated results are retrieved (a luw_get_result sibling exists), which matters for a generation tool.

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?

Two tight sentences: the first delivers the core transformation, the second delivers the cost constraint. Zero filler and no repetition of the title or schema.

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?

With 7 parameters, no output schema, and generation semantics, an agent still lacks information on result retrieval, expected latency, and how the returned variations are ordered or delivered. The credit-cost note partially compensates, but the definition is thin for a tool of this complexity.

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

Parameters3/5

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

Schema description coverage is 100%, so every one of the 7 parameters is already documented in-schema, including enum meanings and the precision scale. The description adds no parameter-level detail beyond the schema, which is acceptable but earns only the baseline 3.

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?

States a concrete transformation ('turn a 3D model view, CAD/BIM screenshot, clay render... into a photorealistic architectural render'), which is a specific verb+resource. It is clear on its own, but never differentiates itself from the very similar sibling luw_sketch_to_render or from luw_generate_image, so it falls short of a 5.

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

Usage Guidelines3/5

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

The description implies its use case by naming acceptable input types (3D views, CAD/BIM screenshots, clay renders, basic visualizations), which steers the agent toward appropriate inputs. However, it gives no explicit when-not guidance and does not mention the near-identical sibling luw_sketch_to_render, leaving the agent to infer which render tool to pick.

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