switch

Apply Magic Hour Portrait

apply_magic_hour_portrait

Read-onlyIdempotent

Golden-hour rim-light editorial portrait. Choose camera: Canon R5 + 85mm f/1.2 or Hasselblad H6D + 80mm. Returns the styled prompt stack for your shot — pair it with generate_image.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`style`	Yes	canon_85mm = Canon R5 portrait standard. hasselblad_80mm = medium-format luxury.
`subject`	No	What you want to shoot. E.g. "a woman walking through a hotel lobby" or "morning coffee on the balcony".

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds useful behavioral context by stating it 'returns the styled prompt stack' and instructs pairing with generate_image, exceeding the baseline set by annotations alone.

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 concise, front-loaded with the core purpose, and each sentence earns its place. It avoids redundancy and stays within three short sentences.

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 tool with two parameters and no output schema, the description adequately explains the purpose, camera choices, return type, and downstream usage with generate_image. It complements the schema and annotations effectively.

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?

Schema coverage is 100%, so parameters are well documented. The description adds extra meaning by mapping the style enum to specific camera bodies (Canon R5 + 85mm f/1.2 vs Hasselblad H6D + 80mm), reinforcing the parameter semantics beyond the schema.

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: it applies a golden-hour rim-light editorial portrait style and returns a styled prompt stack. It distinguishes itself from sibling apply_* tools by specifying the unique 'magic hour portrait' aesthetic and camera options.

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 context on usage: choose a camera and then pair the returned prompt stack with generate_image. It implies the workflow without explicitly excluding alternatives, fitting the 'clear context, no exclusions' level.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All switch tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?