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Glama

Transform Image

transform_image

Apply common image edits—crop, resize, rotate, flip, blur, sharpen, grayscale, or watermark—to prepare images for publishing, analysis, or delivery in PNG, JPEG, or WebP formats.

Instructions

Transform an image using Pillow operations.

Input Schema

NameRequiredDescriptionDefault
xNo
yNo
textNoWatermark text
widthNo
factorNoSharpness factor (default: 2.0, >1 = sharper)
heightNo
radiusNoBlur radius (default: 2.0)
degreesNoRotation angle in degrees
opacityNoWatermark opacity 0.0-1.0 (default: 0.5)
positionNoWatermark position (default: 'bottom-right')bottom-right
directionNoFlip direction ('horizontal' or 'vertical')
operationYesOne of: crop, resize, rotate, flip, blur, sharpen, grayscale, watermark
image_pathYesPath to the image to transform
output_formatNoOutput format (png, jpeg, webp)png
maintain_aspectNoKeep aspect ratio when resizing (default: True)
output_filenameNoCustom output filename (optional)

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.4

TDQS

C2.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure for a mutation-style image tool. It implies image modification but does not state whether the original file is overwritten, where output is written, whether permissions are required, or what side effects occur.

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 single sentence is front-loaded and free of filler, but it is under-specified rather than genuinely concise for a tool with 16 parameters. It does not earn its brevity by conveying enough information.

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 complexity of a 16-parameter image mutation tool with no annotations, the description is far too thin. Although an output schema exists and the input schema documents many parameters, the description still lacks operation semantics, file-handling behavior, and sibling differentiation.

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 75%, so most parameters are documented in the schema, but the description adds no meaning beyond it. With 16 parameters and several undocumented numeric fields such as x, y, width, and height, the one-sentence description does not compensate for the remaining gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a generic verb and resource ("Transform an image") and mentions the underlying library, but "transform" is broad and does not distinguish this tool from siblings like edit_image, advanced_edit, upscale_image, or remove_background. It also omits the actual supported operations that are buried in 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 the many other image-manipulation siblings, nor any prerequisites or exclusions. An agent must infer its role entirely from the name and schema.

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