Compute the relative sharpness of the input image, based on the edge gradients. It's usually used to automate focusing a camera lens on a scene. Note that the score may be affected by the contrast, brightness and content of the scene, thus it's meaningless to compare the sharpness scores of diffe...
AI agents call image_stats_sharpness to retrieve information from Leaper Vision Toolkit without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
imageUrl | string | — | 图片的url地址，通过这个地址获取到输入图像 |
regionJson | string | — | 指定的Roi区域。如果用户没有指定区域，请直接传入 "null"。 如果用户指定区域，传入一个序列化的JSON字符串，属性包括：LpvClassName(调用工具函数的名称) 和 InputParameterFile(调用工具函数需要传入的参数文件名称)。这两个属性的值可以从前处理的MCP工具的返回值中获取。LpvCl |
Parameters from the server's own tool schema.
This tool only analyzes an input image and returns a computed sharpness score. It reads/queries image data and produces a numerical result with no side effects, no data modification, and no external operations triggered.
From the tool's definition Compute the relative sharpness of the input image, based on the edge gradients... Returns a JSON string with the following fields: Sharpness: The sharpness score
Attacks that exploit this kind of access
Compute the relative sharpness of the input image, based on the edge gradients. It's usually used to automate focusing a camera lens on a scene. Note that the score may be affected by the contrast, brightness and content of the scene, thus it's meaningless to compare the sharpness scores of different scenes. 计算输入图像的相对清晰度，基于图像中总体的边缘强度。 清晰度计算通常用于实现对静态场景的自动对焦。 注意清晰度通常受到图像的对比度、亮度以及场景内容的影响，因此无法比较不同场景的清晰度数值。 Returns a JSON string with the following fields: Sharpness: The sharpness score 返回结构是一个序列化的JSON字符串，包含以下字段： Sharpness: 清晰度评价数值. It is categorised as a Read tool in the Leaper Vision Toolkit MCP Server, which means it retrieves data without modifying state.
image_stats_sharpness accepts 2 parameters: imageUrl, regionJson. The full parameter table on this page comes from the server's own tool schema.
Register the Leaper Vision Toolkit MCP server in PolicyLayer and add a rule for image_stats_sharpness: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Leaper Vision Toolkit. Nothing to install.
image_stats_sharpness is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the image_stats_sharpness rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for image_stats_sharpness. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
image_stats_sharpness is provided by the Leaper Vision Toolkit MCP server (leaper-mcp/leaper-mcp-proxy). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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