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AetherWave Studio

by com.aetherwavestudio.mcp

Remove background from video

aetherwave_remove_background_video

Strips the background from a video frame-by-frame using rembg (u2netp) on AetherWave's Python service. Pass a public videoUrl. Choose bgType: "transparent" for an alpha-channel WebM output (compositing) or bgType: "color" with a customColor hex for a solid replacement. 2 credits per second. Slowest tool in the surface (per-frame processing); a 6s clip takes ~4 min, a 30s clip ~15-20 min. Works best on subjects with clear edges (people, products). Returns the processed video URL (R2-hosted).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bgTypeNo'transparent' = alpha WebM output (default). 'color' = solid replacement using customColor.
videoUrlYesPublic URL of the source video (MP4).
customColorNoHex color for solid background when bgType='color' (e.g. '#00ff00'). Default green.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations, the description adds cost (2 credits/second), performance expectations (6s clip ~4 min), limitations (clear edges), and return format (R2-hosted URL). This significantly enriches the behavior profile without contradicting any annotation.

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 a single, dense paragraph with no filler. It front-loads the purpose and progressively provides options, cost, performance, and output. Each clause adds distinct value.

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?

Covers the full lifecycle: input requirements, processing method, output format, cost, and performance caveats. Even without an output schema, it tells the agent what to expect (R2 URL). Given the tool's complexity, this is remarkably complete.

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?

The schema already defines all three parameters with descriptions (100% coverage), but the description adds relational meaning: it explains how bgType and customColor interact (transparent vs color replacement) and specifies the output format (alpha WebM). This goes beyond the schema's individual 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 opens with a specific verb ('Strips') and resource ('the background from a video'), clarifying the frame-by-frame methodology using rembg. This clearly distinguishes it from siblings like aetherwave_remove_background (image-focused) and other video tools.

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?

Provides clear context on when to use it ('Works best on subjects with clear edges') and warns about performance ('Slowest tool in the surface') with concrete timing examples. It does not explicitly name alternatives or exclusion scenarios, but the context is strong 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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct media operation (image, video, audio, listing, mastering, etc.) with clear boundaries. Even similar tools like generate_image and edit_image are differentiated by their primary intent (creation vs. modification) and model selection guidance.

Naming Consistency5/5

All tools follow the 'aetherwave_verb_noun' pattern consistently, using snake_case. Verbs and nouns are descriptive and predictable (e.g., generate_image, list_video_models, remove_background_video).

Tool Count4/5

16 tools is slightly above the ideal range (3-15) but remains well-scoped for a multimedia generation platform covering image, video, audio, and user management. Each tool serves a distinct purpose, and no obvious bloat exists.

Completeness4/5

The tool surface covers core creation, editing, listing, and enhancement workflows for images, videos, and audio. Minor gaps exist (e.g., no delete tool, no get-single-creation tool), but the essential lifecycle is well-covered.

Resources