Skip to main content
Glama

lunaar_product_shot

Transform casual product photos into premium e-commerce shots. AI auto-detects product category and offers four modes: studio white, concept, lifestyle, or product with model.

Instructions

Turn a casual product photo into a premium e-commerce shot. The AI auto-detects the product category — no category input. Pick one of four modes: studio_white (clean white background, marketplace catalog), concept (premium banner / social media scene), lifestyle (in-use / partial human presence — faces avoided), or product_with_model (social-media-ready visual featuring the product with a human model at realistic real-world scale). 10 credits per call. Latency 15-30s. Refunded on provider failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesInitial generation mode. studio_white = white catalog. concept = premium banner / social media. lifestyle = in-use / partial human presence (faces avoided). product_with_model = product shown with a human model at realistic real-world scale (social-media-ready). AI infers the product category from the image.
imagePathYesLocal path to the product photo (jpg/png/webp/heic, ≤25 MB).
aspectRatioNoOptional output aspect ratio. Backend resolves the concrete pixel size; null/omitted lets the model auto-pick.
Behavior5/5

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

No annotations exist, so description carries full burden. It discloses auto-detection of product category, four generation modes, credit cost (10 credits), latency (15-30s), and refund policy on provider failure. This is comprehensive for a creation 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 sentences cover purpose, mode details, and operational constraints (credits, latency, refund). No redundant or extraneous text. Front-loaded with key information.

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

Completeness5/5

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

Given 3 parameters and no output schema, the description covers all inputs, behavior, constraints, and special outcomes (refund on failure). Complete for a generation tool.

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?

Schema coverage is 100%, so baseline is 3. Description adds value by explaining the meaning of each mode beyond schema enums, and clarifies the auto-detect behavior for mode. Aspect ratio auto-pick is also explained.

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?

Description clearly states the tool's purpose: 'Turn a casual product photo into a premium e-commerce shot.' It uses specific verb phrase and resource, and distinguishes from siblings focused on specific product types like jewelry or glasses.

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?

Description explains when to use each of the four modes, but does not explicitly contrast with sibling tools like lunaar_product_shot_variants or provide when-not scenarios. Nonetheless, the mode selection provides clear usage context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Group-Taiga/lunaar-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server