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Hermoso

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by hermoso-ai
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JavaScript
Remote

Set product photo

set_product_image
DestructiveIdempotent

Save a product image as the brand's default product photo, validating product and safety first, so future ad plans and renders use the true packaging.

Instructions

Lock an image as the ad's real PRODUCT photo and SAVE it as this brand's default product, so every later plan_ad / render_ad / generate_image grounds on the true packaging without being told again. 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 or saved (the summary says why). On PASS it persists the photo to a DURABLE url, writes it to the brand's product library as the DEFAULT, and READS THE BRAND BACK to confirm — savedToBrand and the summary report what the brand ACTUALLY holds now, never what was asked for, so if it did not become the default you are told instead of finding out from a paid render. Bills one vision check. Reads YOUR saved brand for the category match (pass brandId to target a specific profile — switches this key's active profile like use_brand).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandIdNoa profile id/name from list_brands to lock the product for; omit to use the active profile
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"

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.374
    • changedInput schema / properties / brandId / description
      Previous value: -"a brand id/name from list_brands to lock the product for; omit to use the active brand"New value: +"a profile id/name from list_brands to lock the product for; omit to use the active profile"
  2. Changed1 schema field changedv0.1.161
    • changedOutput schema / (root)
      Previous value: -{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "additionalProperties": false,
-  "properties": {
-    "attached": {
-      "description": "true when the image passed the product check and was locked",
-      "type": "boolean"
-    },
-    "source_note": {
-      "anyOf": [
-        {
-          "type": "string"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "where the photo came from"
-    },
-    "summary": {
-      "description": "the check verdict — on rejection, why nothing was locked",
-      "type": "string"
-    },
-    "url": {
-      "anyOf": [
-        {
-          "type": "string"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "the durable served URL of the locked product photo"
-    }
-  },
-  "type": "object"
-}New value: +null
  3. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Far exceeds the annotations: discloses the product+safety vision check, the rejection path (lifestyle/off-category/unsafe images lock nothing), durable-URL persistence, the default-write to the brand library, the read-back confirmation via savedToBrand, and the one-vision-check billing. This is exactly the behavioral context an agent needs for a destructive mutation.

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-loads the lock/save purpose and its downstream effect, then layers the check, rejection, and confirmation semantics. Dense and heavily emphasized with caps, but nearly every sentence carries information an agent must not miss.

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?

For a destructive, no-output-schema mutation tool, the description covers the happy path, the failure/rejection path, the returned fields (savedToBrand, summary), billing, and profile targeting. Nothing an agent needs to call it correctly is missing.

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 the baseline is 3, but the description adds real value beyond the schema: concrete source examples for imageUrl and the note that brandId switches this key's active profile like use_brand. source_note is left to the 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?

States a specific verb+resource ('Lock an image as the ad's real PRODUCT photo and SAVE it as this brand's default product') and immediately ties it to downstream effects on plan_ad/render_ad/generate_image, distinguishing it from siblings like list_product_photos and generate_image.

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?

Explains clearly what imageUrl should be (a research result, a workspace/list_product_photos URL, or any public product photo) and how brandId targets a profile like use_brand. It stops short of an explicit when-not-to-use rule, but the context is strong and actionable.

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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