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hermoso-ai

Hermoso

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Set product photo

set_product_image
Idempotent

Lock a product photo for ad rendering by passing an image URL. The server validates the image contains the product and matches the brand category, then returns a durable URL for use in generate_image or render_ad.

Instructions

Lock an image as the ad's real PRODUCT photo so every render grounds on the true packaging. Pass imageUrl = a product shot's URL — an image from a prior research result (an organic Instagram/TikTok post, a scraped page image), a workspace / list_product_photos url, or any public product photo. The server downloads it and runs a product+safety check: a lifestyle/scene shot with no clear product, or an off-category / unsafe image, is REJECTED and NOTHING is locked (the summary says why). On PASS it persists the photo to a DURABLE url and returns it — pass that url as a reference to generate_image / render_ad. Bills one vision check. Reads YOUR saved brand for the category match (pass brandId to target a specific brand — switches this key's active brand like use_brand).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandIdNoa brand id/name from list_brands to lock the product for; omit to use the active brand
imageUrlYesthe image URL to lock as the product (from a research result, a workspace / list_product_photos url, or any public product photo)
source_noteNoa short note on where it came from, e.g. "from their IG post"

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNothe durable served URL of the locked product photo
summaryNothe check verdict — on rejection, why nothing was locked
attachedNotrue when the image passed the product check and was locked
source_noteNowhere the photo came from
Behavior5/5

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

Discloses the check process, rejection reasons, persistence, billing, and brand switching. Annotations are complemented with rich behavioral context beyond readOnlyHint, openWorldHint, idempotentHint, destructiveHint.

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

Conciseness4/5

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

Front-loaded with purpose, but multiple sentences make it slightly long. Still efficient with no wasted phrases.

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?

Covers purpose, input types, behavior, outcomes, and billing. Output schema exists so return values are clear. Complete for a write operation with validation.

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 has 100% coverage; description adds significant context for imageUrl (valid sources), brandId (switching behavior), and source_note. Enriches beyond schema.

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 clearly states the tool locks an image as the product photo. It distinguishes from siblings like list_product_photos by specifying it sets, not lists, the photo.

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?

Provides detailed guidance on when to use (after research), acceptable URL sources, and what happens on rejection/pass. Does not explicitly state when not to use, but context is clear.

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

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