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by ai.switchapp
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Apply High Fashion Editorial

apply_high_fashion_editorial
Read-onlyIdempotent

High-fashion magazine cover/editorial energy. Choose a mood: glossy cover, dark cinematic, hard-flash conceptual, painterly Renaissance, dreamlike fairy-tale, black-and-white natural, or off-duty candid. Returns the styled prompt stack for your shot and the aspect_ratio to make it at — pair it with generate_image and pass that aspect_ratio along.

Input Schema

NameRequiredDescriptionDefault
styleYesglossy_cover = slat-shadow magazine cover gloss. dark_cinematic = deep shadow, flash + ambient, edge of menace. hard_flash_conceptual = direct flash, velvet texture. painterly_renaissance = raking sunlight, Renaissance grade. dreamlike_fairytale = Noctilux chiaroscuro. black_and_white_natural = natural light, no makeup. off_duty_candid = morning window light, relaxed.
subjectNoWhat you want to shoot. E.g. "a woman walking through a hotel lobby" or "morning coffee on the balcony".
aspect_ratioNoOptional frame shape. Replaces the shape the style uses by default. The answer carries aspect_ratio: pass it to generate_image so the picture is made at that shape.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / aspect_ratio
      Added value: +{
+  "description": "Optional frame shape. Replaces the shape the style uses by default. The answer carries aspect_ratio: pass it to generate_image so the picture is made at that shape.",
+  "enum": [
+    "9:16",
+    "16:9",
+    "1:1",
+    "4:5",
+    "5:4",
+    "3:2",
+    "2:3",
+    "4:3"
+  ],
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • changedInput schema / properties / style / description
      Previous value: -"Photographer attribution drives the lighting + camera + grade stack."New value: +"glossy_cover = slat-shadow magazine cover gloss. dark_cinematic = deep shadow, flash + ambient, edge of menace. hard_flash_conceptual = direct flash, velvet texture. painterly_renaissance = raking sunlight, Renaissance grade. dreamlike_fairytale = Noctilux chiaroscuro. black_and_white_natural = natural light, no makeup. off_duty_candid = morning window light, relaxed."
    • changedInput schema / properties / style / enum
      Previous value: -[
-  "testino_glossy",
-  "klein_dark_cinematic",
-  "inez_vinoodh_hard_flash",
-  "leibovitz_painterly",
-  "walker_dreamlike",
-  "lindbergh_bw_natural",
-  "cass_bird_off_duty"
-]New value: +[
+  "glossy_cover",
+  "dark_cinematic",
+  "hard_flash_conceptual",
+  "painterly_renaissance",
+  "dreamlike_fairytale",
+  "black_and_white_natural",
+  "off_duty_candid"
+]
  3. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds valuable context by stating that the tool returns a styled prompt stack plus an aspect_ratio rather than generating the image itself, and by specifying the handoff to generate_image.

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?

The description is three compact sentences, front-loads the use case, lists the moods efficiently, and ends with the key downstream integration step. It is well-sized for the tool’s complexity.

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

Completeness4/5

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

Although there is no output schema, the description explains that the result is a styled prompt stack plus an aspect_ratio. That is enough for an agent to know how to use the output, though it could mention whether the prompt stack has a particular shape.

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%, and the enum descriptions already explain each style and aspect ratio. The description’s mood list mirrors the schema rather than adding new parameter semantics, so the baseline of 3 is appropriate.

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?

The description clearly frames a high-fashion editorial style preset and enumerates seven specific moods, helping distinguish it from generic style tools. It could be sharper by naming a close sibling such as apply_graphic_editorial_portrait, but the intended purpose is clear.

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?

It tells the agent to choose a mood and then pair the result with generate_image, passing along the returned aspect_ratio. That is clear downstream guidance, though it does not state when not to use this tool versus sibling style presets.

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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