switch

Apply Cinematic Anamorphic

apply_cinematic_anamorphic

Read-onlyIdempotent

ARRI Alexa anamorphic widescreen film look. Choose grade: warm golden, cool noir, or moody desaturated. Returns the styled prompt stack for your shot — pair it with generate_image.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`style`	Yes	warm_golden = late-afternoon honey. cool_noir = neon-fill desaturated. moody_desaturated = soft window low-contrast.
`subject`	No	What you want to shoot. E.g. "a woman walking through a hotel lobby" or "morning coffee on the balcony".

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already establish read-only and idempotent behavior. The description adds that the tool returns a styled prompt stack rather than a direct image, and that it should be followed by generate_image, providing useful behavioral context beyond safety hints.

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 two sentences, front-loaded with the core purpose, followed by options and output guidance. Every word contributes, with no redundancy or fluff.

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 simple read-only tool with no output schema, the description covers purpose, output (prompt stack), usage context (pair with generate_image), and parameter choices. Annotations cover side-effect safety, making this sufficiently complete.

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%, so the baseline is 3. The description mentions 'Choose grade: warm golden, cool noir, or moody desaturated' but does not add information beyond the schema's detailed enum descriptions. The 'for your shot' phrase weakly hints at the subject parameter, but adds little value.

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 applies an ARRI Alexa anamorphic widescreen film look, giving a specific verb and resource. It differentiates from sibling apply_* tools by naming the exact cinematic style and available grades.

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 explicitly says 'pair it with generate_image', indicating when to use the tool as a preparatory step for image generation. It does not mention alternatives or exclusions, but the style-specific context is clear enough to guide selection.

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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