Learn the pattern template features from the provided image. 训练模板，使用输入的图像。 The template center can be modified later. 模板中心可根据需求修改。 The shape and polarity of the template features are extracted from the shape image. 特征点通过给定的形状图像提取提取模板的形状和极性。 The feature points are extracted from the given shape im...
AI agents use pat_match_learn_with_shape_image to create or update resources in Leaper Vision Toolkit — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Leaper Vision Toolkit environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
imageUrl | string | — | 图片的url地址，通过这个地址获取到需要输入的图像。需提醒用户先设置 GrayValueWeight 并不启用任何灰度特征权重。可为空，但当其为空时，请直接传入 "null"。 |
shapeImgUrl | string | — | 图片的url地址，通过这个地址获取到需要输入的形状图像。该图像的尺寸需与 imageUrl图像的参数相同,如果用户不认可结果，可以提醒用户尺寸问题。 |
matchClassObjDescriptionJson | string | — | 模板匹配的功能对象用于训练模板匹配的功能。如果用户没有指定模板匹配的功能对象，请创建一个模板匹配的功能对象后传入。 如果用户指定模板匹配的功能对象，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)。 |
regionClassObjDescriptionJson | string | — | 输入的Roi区域，用于限定模板在输入图像中的位置，并用于剔除形状中超出区域的部分。如果用户没有指定该Roi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 |
Parameters from the server's own tool schema.
This tool creates and persists a new pattern template resource based on input images. While it does not delete or destroy existing data (ruling out Destructive), and does not execute arbitrary code or trigger unpredictable operations (ruling out Execute), it clearly writes a new configuration object. The impact is reversible—templates can be replaced or removed—so it falls under Write rather than a higher category.
From the tool's definition Tool description states 'Learn the pattern template features from the provided image' and 'Training template, using input image' (训练模板，使用输入的图像). Returns a 'serialized JSON string' containing template parameters.
Attacks that exploit this kind of access
Learn the pattern template features from the provided image. 训练模板，使用输入的图像。 The template center can be modified later. 模板中心可根据需求修改。 The shape and polarity of the template features are extracted from the shape image. 特征点通过给定的形状图像提取提取模板的形状和极性。 The feature points are extracted from the given shape image, thus not affected by DetailLevel. 特征点通过给定的形状图像提取，不受 DetailLevel 参数的影响。 The shape image should have the same size as the input image. 形状图像的尺寸需与输入图像相同。 如果别的工具需要训练后的模板匹配的功能对象，可以使用当前对象Json。 返回结构是一个序列化的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)。 LpvClassName 表示模板匹配的功能对象名称，目前名称应为：ILMatch (模板匹配的功能对象); InputParameterFile 表示模板匹配的功能对象的参数文件名称。. It is categorised as a Write tool in the Leaper Vision Toolkit MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
pat_match_learn_with_shape_image accepts 4 parameters: imageUrl, shapeImgUrl, matchClassObjDescriptionJson, regionClassObjDescriptionJson. 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 pat_match_learn_with_shape_image: 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.
pat_match_learn_with_shape_image is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the pat_match_learn_with_shape_image 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 pat_match_learn_with_shape_image. 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.
pat_match_learn_with_shape_image 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.
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