YouCam for Creators

Ai Photo Background Blur

AI-Photo-Background-Blur

The bokeh effect is a popular photographic technique used to blur the background of a photo and bring the subject into focus. It adds an artistic touch to a photograph, making it look more professional and eye-catching. Create professional-looking photos with the AI Photo Background Blur API, which automatically isolates subjects and applies a natural background blur to draw attention where it matters most. Sample Usage Scenarios:

Portrait Enhancement Apply a natural bokeh effect to make subjects stand out and improve the visual quality of profile or portrait photos. Before: After:
Professional Headshots Create studio-like background blur effects from standard photos for business profiles and corporate directories. Before: After:

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

A3.7/5.0

Behavior3/5

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

The description adds the behavioral detail that it 'automatically isolates subjects', which goes beyond the annotations. With annotations already indicating mutability and non-destructiveness, this is adequate but not rich; it omits async processing or input requirements.

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 relatively concise and well-structured, with the core purpose stated early and sample scenarios in a clean list. The opening sentence about bokeh is educational but not essential, slightly reducing conciseness.

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?

Given the tool's complexity (async execution, multiple input methods), the description provides useful use case context but omits details about input requirements and task lifecycle. The schema covers these gaps, so the description is sufficient for selection but not fully complete on its own.

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?

The schema description coverage is only 50%, with the 'request' parameter lacking a top-level description. The description does not explain 'request', 'polling', 'intensity', or 'src_file_url/src_file_id', so it fails to compensate for the gap and adds no parameter semantics.

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 explicitly states the tool 'applies a natural background blur' to photos and 'automatically isolates subjects', clearly defining its function. It distinguishes itself from sibling tools like background removal or change by focusing on blur, and the verb-resource pairing is unambiguous.

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?

Sample usage scenarios (Portrait Enhancement, Professional Headshots) provide clear context on when to use this tool. However, it does not explicitly mention when not to use it or suggest alternatives, so it falls short of a 5.

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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