switch

Generate Image

generate_image

Generate one or more Switch images. Auto-routes to the right model based on subject (Nano Banana 2 default, GPT Image 2 for swimwear/beach, Switch Model/Ultra/Pro for sexier content, Nano Banana Pro for typography-heavy). Counts <= 8 render inline in chat; counts > 8 queue to your Switch Studio with progress polling. All images persist to your Studio library and folder. Pass an optional style (e.g. "wellness/warm_amber_tropical", "high_fashion_editorial/testino_glossy", "movie_scene/neon_noir_action") to apply a curated photographic stack from the apply_* skill tools.

Input Schema

TableJSON Schema

Name	Required	Description
`count`	No	How many images to generate. Default 4. <= 8 returns inline, > 8 queues to Studio. Beta limit: max 50 per request — larger asks are capped at 50 and the response says so.
`model`	No	Optional explicit model. If omitted, auto-routed based on subject content (see tool description).
`style`	No	Optional curated style stack from the apply_* skill tools. Format "<skill>/<style_key>", e.g. "wellness/warm_amber_tropical" or "high_fashion_editorial/leibovitz_painterly".
`subject`	Yes	Plain-English description of what to generate. E.g. "a woman walking through a hotel lobby" or "morning coffee on the balcony, model wearing a robe".
`folder_name`	No	Optional Switch Studio folder name. Auto-created if missing. Defaults to the chat-derived title.
`aspect_ratio`	No	Image aspect ratio. Default 9:16 (vertical, social-friendly).
`real_photo_look`	No	Optional. Adds the casual real-photo texture (film grain, amateur iPhone feel). OFF by default — only set true when the user asks for the realistic, unpolished look.
`face_reference_ids`	No	Face reference asset ids from upload_reference_asset (frame_type "face"). The ONLY way to use a face/likeness reference. Each id is verified server-side (your own untouched original + identity verification) before anything generates or is charged; a URL or generic upload here is rejected.
`reference_image_urls`	No	Optional public image URLs used as GENERIC references (products, scenery, outfits, style). These are never treated as face references — for a person's face/likeness use face_reference_ids.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`asset`	No
`images`	No
`_widget`	No

TDQS

A4.6/5.0

Behavior5/5

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

Despite only minimal annotations (readOnlyHint=false), the description discloses important behaviors: auto-routing to models, inline vs. queued processing with progress polling, and persistence of images to Studio library/folder. This adds substantial context beyond the annotation and gives the agent a clear mental model of side effects.

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 four sentences, front-loaded with the main action, and every sentence contributes meaningful information (what, models, count behavior, persistence, style). It is concise without sacrificing important operational details.

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?

For a complex tool with 9 parameters and a rich schema, the description covers the essential workflow: generation, model selection, count handling, persistence, and style integration. It doesn't repeat schema details, and since an output schema exists, return values are already documented. It is sufficiently complete for an agent to understand the tool's primary use.

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?

With 100% schema description coverage, the baseline is satisfied. The description adds value by explaining count behavior (<=8 vs >8), model auto-routing logic, and style format with concrete examples. It enriches the semantic meaning of key parameters beyond the schema descriptions.

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 opens with 'Generate one or more Switch images,' which clearly states the verb, resource, and scope. This distinguishes the tool from sibling audio/video generation tools and clarifies its core function. Additional details about model auto-routing further specify the purpose.

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?

The description provides clear guidance on when to use this tool (image generation) and offers behavioral guidance such as count thresholds (<=8 inline, >8 queue) and model routing logic. It also explains how to use style from apply_* tools, but it doesn't explicitly name alternative tools for audio/video, though those are obvious from sibling names.

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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TDQS

A3.5/5.0

Disambiguation2/5

Several tools occupy nearly identical semantic ground: apply_iphone_realism and apply_ugc both describe casual phone-shot looks, upload_media and upload_reference_asset both accept uploads, and analyze_video overlaps heavily with analyze_video_report. The many apply_* style tools are essentially one tool parameterized by style, so agents can easily select the wrong one.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern such as generate_image, list_my_videos, get_editor_run, and upscale_video. A few outliers like voice, talking_avatar_video, and video_to_prompt do not use the same verb-first convention, but they are still readable and do not create significant confusion.

Tool Count1/5

At 55 tools, the surface is far beyond what is appropriate for an MCP server; many of these be collapsed or parameterized, especially the 10 apply_* style wrappers and several overlapping upload/status helpers. Even for a broad media platform, this scale forces a huge context window and makes selecting the right tool impractical.

Completeness2/5

The surface covers generation, media display, video analysis, and Editor workflows well, but there are obvious gaps in library lifecycle management: move_asset and create_folder are referenced in tool descriptions without being exposed, and there is no clean way to delete or reorganize media assets. Agents following the descriptions will try to call tools that do not exist.

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