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switch

by ai.switchapp

Create Depth Map

create_depth_map

Turn a video into a DEPTH MAP: a grayscale video where brightness encodes distance, used as a motion reference so a new generated subject moves exactly like your source clip. Pass video_url (a public https video URL) OR one of your own Switch video ids (from list_my_videos or list_my_assets). For an external URL also pass duration_seconds (the clip length; your own Switch videos carry it automatically) because the render is billed per second of video. Returns a task_id right away; poll get_depth_map_status until the download URL is ready (usually a few minutes). If the render fails, your tokens are returned automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_urlYesA public https video URL, OR one of your own Switch video ids.
duration_secondsNoClip length in seconds. Required for external URLs; your own Switch videos are measured automatically.

TDQS

A4.1/5.0
Behavior4/5

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

With readOnlyHint=false, the description correctly indicates a mutating operation (video to depth map). It adds valuable behavioral context: billing per second, immediate task_id return, asynchronous processing with polling, and automatic token refund on failure. This goes beyond the annotation and is transparent about cost and failure behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact yet dense, covering purpose, input options, billing, async workflow, and failure handling without fluff. Each clause contributes meaningful information. It could be slightly shorter, but the structure is logical and front-loaded.

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 an async creation tool with no output schema, it covers the key workflow: what it produces, how to pass videos, billing implications, immediate task_id, polling via get_depth_map_status, and token refund on failure. It doesn't describe the response JSON structure, but that's not necessary given the status tool and the task_id return.

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% and the schema descriptions already explain that video_url can be an external URL or a Switch video id, and duration_seconds is required for external URLs. The description reinforces this but does not add significant new parameter-level detail beyond the schema's own descriptions.

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 uses a specific verb ('Turn a video into') and clearly defines the output as a depth map, a grayscale video encoding distance. It distinguishes this from sibling tools like get_depth_map_status (status polling) and analyze_video by focusing on depth map generation.

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 clearly explains how to provide input via external URL or own Switch video ids, naming list_my_videos and list_my_assets for retrieving ids. It also specifies when duration_seconds is required and points to get_depth_map_status for polling. It lacks an explicit 'when not to use' but the context is sufficient.

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