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 is described with the given shape region. 模板的形状和极性通过输入的形状区域来定义。 Adding a region produce a white-on-black shape while s...
AI agents use pat_match_learn_with_shape 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"。 |
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 |
shapeRegionClassObjDescriptionJson | string | — | 输入的Roi区域，用于生成模板形状。 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。LpvClassName 表示要调用的R |
Parameters from the server's own tool schema.
The tool generates and persists new template objects derived from input images—this is data creation, fitting the Write category. It does not execute arbitrary code, delete data, or access financial systems. The severity is medium because misuse could create incorrect or adversarial pattern templates that mislead downstream image analysis, but the impact is localized to the pattern matching domain.
From the tool's definition Tool description states it will "Learn the pattern template features from the provided image" and "produce a white-on-black shape while subtracting a region produce a black-on-white shape." The tool creates and stores a trainable pattern template object…
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 is described with the given shape region. 模板的形状和极性通过输入的形状区域来定义。 Adding a region produce a white-on-black shape while subtracting a region produce a black-on-white shape. 添加区域（Add）生成黑色背景上的白色形状；减去区域（Subtract）生产白色背景上的黑色形状。 The feature points are extracted from the given shape region, thus not affected by DetailLevel. 特征点通过给定的形状区域提取，不受 DetailLevel 参数的影响。 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 accepts 4 parameters: imageUrl, matchClassObjDescriptionJson, regionClassObjDescriptionJson, shapeRegionClassObjDescriptionJson. 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: 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 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 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. 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 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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