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Ai Photo Background Change

AI-Photo-Background-Change

The AI Photo Background Change API enhances images by isolating the subject from the original background, enabling a wide range of applications including product-focused use cases in business. This API enables developers to replace the background using custom prompts or predefined templates. Sample Usage Before: After: Before: After:

Input Schema

TableJSON 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

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

B3.3/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, and the description adds that the API isolates the subject and supports prompt/template modes. However, it does not disclose processing behavior such as async polling, rate limits, or image constraints, leaving a gap beyond what annotations already provide.

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 filler ('enhances images', 'wide range of applications') and the 'Sample Usage' section consists of empty 'Before: After:' placeholders that provide no value. The text is not tightly focused and undermines the overall usefulness.

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 two input modes, polling, and template options, the description does not explain when to choose this tool over siblings, prerequisites like listing templates first, or expected workflow. The output schema exists, but the description alone is insufficient for reliable tool selection and invocation.

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 partially conveys the key parameter distinction between using 'custom prompts' and 'predefined templates,' which maps to the type/prompt/template_id fields. It does not explain src_file_url vs src_file_id or polling behavior, and with 50% schema description coverage, the description only marginally compensates for the 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 clearly states the tool replaces the background while isolating the subject, naming the specific resource and action. It distinguishes itself from sibling tools like AI-Photo-Background-Removal and AI-Photo-Background-Blur by explicitly saying 'replace the background using custom prompts or predefined templates.'

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

Usage Guidelines3/5

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

The description implies usage for background replacement and mentions business/product-focused use cases, but it does not explicitly state when not to use this tool or compare alternatives like background removal or blur. No exclusions or alternative tool references are provided.

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.

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