Prune the trained template features. 从已训练的模板特征中，剔除某些指定的特征。 Eliminate the features which are located outside of the given region or belong to the shape polylines of the given indexes. 输入区域对象外的特征将被删除，不参与匹配。 Pass in null region and empty shape indexes to restore the features to original. 传入空区域及 0 过滤...
AI agents use pat_match_prune 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 |
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，输入Roi区域对象外的特征将被删除，不参与匹配。 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。Lpv |
Parameters from the server's own tool schema.
This tool creates or modifies image processing template objects by pruning (removing) features from trained templates. The modifications are reversible via the restore mechanism ('Pass in null region and empty shape indexes to restore').
From the tool's definition Tool description states: 'Prune the trained template features' and 'Eliminate the features which are located outside of the given region' and 'Pass in null region and empty shape indexes to restore the features to original.' The tool modifies trained template…
Attacks that exploit this kind of access
Prune the trained template features. 从已训练的模板特征中，剔除某些指定的特征。 Eliminate the features which are located outside of the given region or belong to the shape polylines of the given indexes. 输入区域对象外的特征将被删除，不参与匹配。 Pass in null region and empty shape indexes to restore the features to original. 传入空区域及 0 过滤阈值，可重置特征为训练后的原始特征。 It may fail if all the features are removed. 剔除过程可能失败，如所有有效特征均被剔除。 如果别的工具需要剔除某些指定的特征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_prune accepts 3 parameters: imageUrl, 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_prune: 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_prune 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_prune 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_prune. 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_prune 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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