switch

Apply Travel

apply_travel

Read-onlyIdempotent

Luxury travel + hotel editorial. Real architecture is preserved exactly (no inventing buildings). Choose subject: hotel hero, rural property, scenic view, drone aerial, lifestyle moment, or interior. If you attach a reference image of a real property, the architecture lock kicks in automatically. Returns the styled prompt stack for your shot — pair it with generate_image.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`style`	Yes	hotel_hero = property is the star. rural_property = country estate. scenic_view = pure landscape. drone_aerial = top-down or 45° from above. lifestyle = model + destination. interior = inside the property.
`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?

Beyond the readOnly and idempotent annotations, the description adds valuable behavioral details: real architecture is preserved exactly (no invented buildings), and attaching a reference image triggers an architecture lock. It also clarifies that the tool returns a prompt stack, not an image, which sets expectations for downstream use.

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?

Five sentences, each earning its place: category, constraint, subject options, reference-image behavior, and output/next-step. The description is front-loaded with the most important information and has no filler.

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 two-parameter style tool with strong annotations and no output schema, this description is fully sufficient. It explains the return type, the core architecture-preservation behavior, the subject list, and how to use the result with generate_image. No critical information is missing.

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%, so both parameters are already well-documented. The description echoes the enum values but doesn't add significant new meaning. The mention of a reference image is useful but not tied to any schema parameter, creating slight ambiguity.

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 luxury travel/hotel editorial style and returning a styled prompt stack. It differentiates from sibling apply_* tools by focusing on the travel domain and the architecture-preservation rule. The title and description together make the purpose unambiguous.

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 gives strong usage context by specifying the domain (luxury travel/hotel editorial), listing subject options, and explaining the pairing with generate_image. It doesn't explicitly name alternative tools or state when not to use it, but the context is clear enough for an agent to decide.

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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