Skip to main content
Glama

Ai Photo Lighting

AI-Photo-Lighting

Brighten your images with Our AI image brightening tool effortlessly. With the legendary AI technology, lighten up any image of your choice. Brighten your dark photos or images with our AI Photo Lighting tool, illuminating your memories in a flash. Before After Brighten low-light photos effortlessly using AI tool, bringing out stunning details and vibrant colors. Before After Brighten your product pictures with AI Lighting tool for a captivating and stunning presentation.

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

B3.4/5.0
Behavior3/5

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

The annotations indicate a non-read-only operation (readOnlyHint: false). The description adds the specific effect (brightening) but no further behavioral details such as output format, file handling, or side effects. It does not contradict annotations, and the stated transformation is consistent with the tool's purpose.

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 repeats the same message four times, including placeholder 'Before After' text. It is not front-loaded and contains substantial redundancy; a single concise sentence would have sufficed.

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?

The tool involves a compound `request` object with two input modes (URL or file ID) and a polling option, but the description only addresses the purpose. It omits how to supply input and any technical constraints, relying entirely on the schema for invocation details.

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 input schema describes `polling`, `src_file_url`, and `src_file_id` with examples, but the description is entirely marketing-focused and provides no guidance on constructing the `request` object or other parameters. With only 50% top-level schema coverage, the description does not compensate for the undocumented `request` parameter.

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 repeatedly and explicitly states the tool 'brightens' or 'lightens' images, naming it an 'AI Photo Lighting tool'. This clearly identifies the function as adjusting lighting/brightness, distinguishing it from siblings like AI-Color-Correction or AI-Photo-Enhance.

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 concrete use cases: 'dark photos', 'low-light photos', and 'product pictures'. It implies when to use the tool, though it does not explicitly compare to alternatives or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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