Image Crop
image-cropDrag-to-select cropping with preset aspect ratios. (Browser-based tool)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
image-cropDrag-to-select cropping with preset aspect ratios. (Browser-based tool)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It reveals the interaction model (drag-to-select, preset ratios) but omits critical context such as output format, whether the original image is modified, persistence, or system requirements. This is a significant gap for a tool that likely manipulates images.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two terse phrases that convey the core functionality. Every word contributes meaning, and it is appropriately front-loaded with the key verb ('cropping') and interaction model.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, interactive browser tool with no parameters and no output schema, the description is relatively complete. It communicates what the tool does and how the user interacts with it. However, it could benefit from a sentence about what the crop output is or how to retrieve the result, so it is not perfect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline is 4 per the rubric. The description adds value by clarifying that the tool is browser-based and interactive, which explains why no server-side parameters are needed. No additional parameter details are possible since none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs cropping via drag-to-select with preset aspect ratios, giving the specific verb and resource. It distinguishes itself from related tools like image-resizer or smart-resize by specifying cropping and the browser-based interaction, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The only usage signal is the phrase 'Browser-based tool,' which implies user interaction but does not explain when to choose this tool over other image editing tools. There is no explicit when-to-use, when-not-to-use, or mention of alternatives, leaving the agent without clear guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.
Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.
202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.
The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.