switch

Apply Movie Scene

apply_movie_scene

Read-onlyIdempotent

Put me in a movie — full cinematic film look matching specific film genres. Choose: neon-noir action thriller, 80s finance excess, comic-book superhero blockbuster, video-game key art, or generic action thriller. Returns the styled prompt stack for your shot — pair it with generate_image.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`style`	Yes	neon_noir_action = wet streets + neon + anamorphic. glamour_finance_excess = 1980s Wall Street mahogany / gold. superhero_blockbuster = comic-book key art. video_game_character = Unreal-Engine character render. generic_action_thriller = ARRI cinematic.
`subject`	No	What 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?

The description discloses that the tool returns a styled prompt stack, which goes beyond the readOnlyHint and idempotentHint annotations. This clarifies it is a safe, side-effect-free operation that only generates content for later use, improving agent understanding of the tool's behavior.

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, each with a distinct purpose: stating the function, listing options, and explaining the return value and next step. It is concise, well-structured, and free of redundant information.

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 tool with two parameters and no output schema, the description covers purpose, options, return value, and next-step guidance. The only minor gap is the exact structure of the 'styled prompt stack,' but this is likely evident from the generate_image context. Overall, it is complete enough for its complexity.

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?

The input schema already provides comprehensive descriptions for both parameters, including detailed enum explanations. The description adds no additional parameter semantics; it merely lists the same genres. Given 100% schema coverage, the baseline of 3 is appropriate.

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's function: applying a cinematic film look with specific genre choices, listing five distinct styles. It distinguishes from sibling apply_* tools like apply_travel or apply_product by focusing on movie scene genres. The verb 'apply' and resource 'movie scene' are explicit.

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 explicitly instructs to pair the output with generate_image, indicating the intended workflow. It provides a choice of styles, giving context on when to use it. However, it does not explicitly exclude other apply_* tools or mention alternatives, so it is clear but not exhaustive.

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

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