矩形裁剪图片（1 credit/次）。先用 img_info 看尺寸再定坐标。产物存文件中转站返下载 URL。失败自动退款。
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | 输出格式 png/jpeg/webp；缺省跟随源格式 | |
| top | Yes | 裁剪起点 Y(px) | |
| left | Yes | 裁剪起点 X(px，左上角为原点) | |
| width | Yes | 裁剪宽(px)，越界自动收敛到图内 | |
| height | Yes | 裁剪高(px) | |
| file_id | No | 已上传的图片 ID（与 data_base64 二选一） | |
| filename | No | 可选输出文件名 | |
| data_base64 | No | 图片内容 base64（与 file_id 二选一） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the credit cost, that output is stored in a file transfer station returning a download URL, and that failed operations auto-refund. These are meaningful behavioral details beyond the basic crop action, though it does not describe side effects or error cases in depth.
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 three concise sentences, each providing essential information: the action, the prerequisite step, and the output/refund behavior. It is front-loaded and contains no redundant text.
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 tool with 8 parameters and no output schema, the description covers the key workflow: check size first, perform crop, receive URL, and get refund on failure. It does not explain the return format in detail, but the output URL is mentioned. Overall it is complete enough for most use cases.
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?
Schema description coverage is 100%, providing detailed meaning for every parameter (e.g., left/top as starting point, width/height with auto-convergence). The description itself adds no additional parameter-specific context, so a baseline of 3 is appropriate.
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 rectangular cropping on images (矩形裁剪图片), using a specific verb and resource. It distinguishes itself from sibling tools like img_resize, img_rotate, and img_compress by specifying the action and workflow.
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?
It gives explicit guidance to first use img_info to check dimensions before setting coordinates, which is a practical workflow tip. It also mentions cost (1 credit) and output URL, but does not explicitly state when not to use this tool or alternatives for cropping scenarios.
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.
Most tools are clearly distinct, but img_compress and img_convert both support format changes, and img_info and img_exif both read metadata, creating minor overlap. The descriptions help clarify the primary purposes, so ambiguity is low.
All tools share the img_ prefix and mostly use verb-based names like img_compress and img_resize. However, img_info and img_exif are noun-based, which is a slight deviation from the pattern, though still predictable and readable.
With 8 tools, the server is well-scoped for an image toolbox. Each tool addresses a common operation without unnecessary additions, making the count appropriate for the stated purpose.
The toolset covers the core image lifecycle: inspecting info, reading EXIF, compressing, converting, resizing, cropping, rotating, and watermarking. There are no glaring gaps for a practical image toolbox, so agents can handle typical workflows end-to-end.