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by ai.switchapp

Apply Iphone Realism

apply_iphone_realism
Read-onlyIdempotent

Phone-shot amateur look — looks like a real person snapped it on their phone. Casual, candid, pore-level real, no professional gloss. Three flavors: digital phone, 35mm film point-and-shoot, or off-duty intimate. Returns the styled prompt stack for your shot — pair it with generate_image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleYesdigital_phone = Sony A7IV + 50mm f/1.4 GM phone-style realism. film_pointshoot = Contax T2 35mm Portra 400. off_duty_intimate = Cass Bird natural-window editorial.
subjectNoWhat you want to shoot. E.g. "a woman walking through a hotel lobby" or "morning coffee on the balcony".

TDQS

A4.2/5.0
Behavior4/5

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

Annotations provide readOnlyHint and idempotentHint. The description adds key behavioral context by disclosing that the tool returns a prompt stack rather than an image, and details the three style variants. No contradictions with annotations.

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 sentences, front-loaded with the core purpose. Each sentence earns its place: aesthetic definition, flavor list, and output/workflow. No fluff or redundancy.

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 simple 2-parameter tool with no output schema, the description covers purpose, flavor options, and the essential workflow (pair with generate_image). It could be more explicit about the output stack structure, but the combination of schema and description is adequate.

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 coverage is 100% with detailed enum descriptions (e.g., 'digital_phone = Sony A7IV + 50mm f/1.4 GM phone-style realism') and subject examples. The description merely restates the three flavor names without adding new parameter meaning, so it stays at the baseline.

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 creates a 'phone-shot amateur look' with specific aesthetic qualities ('casual, candid, pore-level real'), and lists three distinct flavors. It also states it 'returns the styled prompt stack' for pairing with generate_image, distinguishing it from sibling apply_* tools.

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 clear context for when to use (when a realistic, candid phone aesthetic is desired) and instructs to 'pair it with generate_image.' It does not explicitly list alternatives or exclusions, but the specialized purpose makes the usage obvious.

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.

Resources