YouCam for Creators

Ai Object Removal Pro

AI-Object-Removal-Pro

Remove unwanted objects with precision from your photos while preserving intricate details. To use this amazing feature, simply input a photo along with a grayscale mask where white pixels indicate foreground elements and black pixels represent background areas. The AI Object Removal then leverages advanced algorithms to produce natural-looking images by effectively removing unwanted objects such as people, reflections, shadows, and other distractions from your photos. Sample input: Sample output: Sample input: Sample output:

Input Schema

TableJSON Schema

Name	Required	Description	Default
`polling`	No	If true (default), keep polling until the task finishes, returning the final result. If false, return immediately without waiting for the task to finish.
`request`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

B3.3/5.0

Behavior2/5

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

The description does not disclose important behavioral traits such as the asynchronous task nature (hinted by the polling parameter), potential delays, or any side effects. Annotations are minimal (openWorldHint=true) but do not explain what that means, and the description adds no operational context beyond the removal process.

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

Conciseness2/5

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

The description contains promotional fluff ('amazing feature', 'leverages advanced algorithms') and broken placeholders ('Sample input:' and 'Sample output:' with no content), which wastes space. The core sentence is clear but the overall structure is not tight or fully professional.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complex input schema with four request variants and the polling parameter, the description is incomplete. It does not mention the async behavior, the different file URL/ID combinations, or how the task lifecycle works. The presence of an output schema helps with return values, but invocation details remain underdocumented.

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 description explains the mask semantics (white pixels = foreground, black pixels = background) and mentions inputting a photo, which partially compensates for the missing description on the 'request' parameter (schema coverage 50%). However, it does not cover the polling parameter or the option to use file IDs instead of URLs, leaving some gaps.

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 and resource: 'Remove unwanted objects with precision from your photos.' It clearly distinguishes this from siblings like AI-Video-Object-Removal by specifying photos, and the mask-based object removal is a unique capability not shared by other photo 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?

The description provides explicit context on when to use the tool: when you need to remove people, reflections, shadows, or distractions from photos. It also gives a clear how-to (photo + grayscale mask) but does not explicitly mention alternatives or when not to use it, such as for videos.

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

C2.4/5.0

Disambiguation3/5

Many tools are clearly distinct (e.g., AI-Object-Removal-Pro vs AI-Replace), but there is notable overlap among upload-related tools (File-Upload, Get-Upload-API-Info, upload_file) and among photo enhancement tools (Enhance, Color-Correction, Lighting) that could cause misselection. Template-listing tools are repetitive but each is tied to a specific generator.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use PascalCase with dashes (AI-Avatar-Generator), some use verb-first patterns (Get-Feature-Cost, Get-Running-Task-Status), and one uses lowercase snake_case (upload_file). The AI- prefix is consistent for many tools, but the overall pattern is mixed.

Tool Count2/5

With 34 tools, the server feels overloaded. Many tools are variants of similar operations (e.g., numerous template listing tools) and could be consolidated or eliminated. The count exceeds the 25+ threshold for 'too many'.

Completeness4/5

The tool surface covers a broad range of AI media editing operations: photo and video generation, enhancement, background editing, face swap, object removal, and upload/status management. Minor gaps like video background removal (only replacement available) exist, but core workflows are well-supported.

Resources

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