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Ai Watermark Removal

AI-Watermark-Removal

AI-Powered Watermark Removal Reliably detect and erase watermarks, logos, and other marks while preserving the image's original quality and details. > Use the tool responsibly to avoid unethical applications, such as removing watermarks for unauthorized commercial use. Respect the intended purpose of the image and adhere to fair use principles.

Input Schema

NameRequiredDescriptionDefault
pollingNoIf true (default), keep polling until the task finishes, returning the final result. If false, return immediately without waiting for the task to finish.
requestYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly=false, openWorld=true, idempotent=false, and destructive=false, so the safety profile is covered. The description adds that the tool removes watermarks/logos/marks while preserving original image quality, which is useful transformation context, but it omits operational behavior such as async polling, file requirements, or output handling.

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

Conciseness3/5

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

The description is short and the main purpose is front-loaded, but the first line repeats the tool name/title, and the ethical blockquote consumes space without helping invocation. It is serviceable but not maximally efficient.

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

Completeness3/5

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

For a simple two-parameter tool with an output schema and annotations, the purpose is clear and safety is covered. However, the description omits usage guidelines, parameter explanation, and async/polling behavior, leaving meaningful gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50%, and the description contributes no information about the polling flag or the request object's src_file_url/src_file_id alternatives. With half the parameters undocumented and no compensating detail in the description, an agent gets limited help beyond the schema itself.

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 states a specific verb+resource ('detect and erase watermarks, logos, and other marks') and clearly scopes the tool to image watermark removal. This distinguishes it from siblings like AI-Object-Removal-Pro and AI-Photo-Background-Removal without requiring the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as Object Removal or Background Removal. The only direction is an ethical reminder about responsible use, which does not help an agent select or route to this tool.

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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